Search This Blog

Sunday, November 5, 2023

Involuntary treatment

From Wikipedia, the free encyclopedia

Involuntary treatment refers to medical treatment undertaken without the consent of the person being treated. Involuntary treatment is permitted by law in some countries when overseen by the judiciary through court orders; other countries defer directly to the medical opinions of doctors.

Some countries have general legislation allowing for any treatment deemed necessary if an individual is unable to consent to a treatment due to lack of capacity, other legislation may specifically deal with involuntary psychiatric treatment of individuals who have been diagnosed with a mental disorder. Psychiatric treatment normally happens in a psychiatric hospital after some form of involuntary commitment, though individuals may be compelled to undergo treatment outside of hospitals via outpatient commitment.

The diagnosis of mental disorders can be carried out by some form clinical practitioner, or in some cases law enforcement or others, to be a danger to themselves or to others is permitted in some jurisdictions, while other jurisdictions have more recently allowed for forced treatment for persons deemed to be "gravely disabled" or asserted to be at risk of psychological deterioration.

A patient may be detained because they are diagnosed with a psychiatric disorder or infectious disease.

History

Man in restraint chair in an English asylum in 1869

In the early 20th century, many countries passed laws allowing the compulsory sterilization of some women. In the USA more than half the states passed laws allowing the forced sterilization of people with certain illnesses or criminals as well as sterilization based on race. Forcible sterilization took place in the United States until at least 1981, more than 64 thousand people were forcibly sterilized. Denmark sterilized 60 thousand people between 1935 and 1976. During Nazi rule in Germany as part of their eugenics program about 600 thousand people were compulsorily sterilized.

Involuntary euthanasia was carried out in Nazi Germany for those who had certain psychiatric disorders or learning disabilities as part of the Aktion T4 program. This program was run by Karl Brandt, a medical doctor, and Philipp Bouhler. Victims were murdered together in gas chambers and this program was a prototype for the extermination camps such as Auschwitz where the Holocaust took place. As part of Action 14f13, physicians involved in the euthanasia program visited concentration camps where they looked at documentation provided by SS camp doctors and approved the murder of camp inmates on the grounds of race, behavior and ability to work using the euthanasia program's facilities.

In the UK the 1950s, homosexual men were given the choice between hormone therapy with female sex hormones or prison including, notably, Alan Turing.

Until 2004, every European state required that transgender people must be sterilized or provably infertile to have their preferred gender formally recognised. This practice continued in Sweden until 2012 and Denmark until 2014 Japan currently requires transgender people to be sterilized and have their ovaries removed to be recognised as a different gender.

Ethics and the law

The Hippocratic Corpus, an ancient Greek text discussing medical ethics, advises that physicians conceal most information from patients to give the patients the best care. The 1767 English case Slater vs Baker and Stapleton found against two doctors who had refractured a patient's leg without consent. Thomas Percival was a British physician who published a book called Medical Ethics in 1803, which makes no mention of soliciting for the consent of patients or respecting their decisions. Percival said that patients have a right to truth, but when the physician could provide better treatment by lying or withholding information, he advised that the physician do as he thought best. Benjamin Rush, an 18th-century United States physician, in a lecture entitled "On the duties of patients to their physicians", stated that patients should be strictly obedient to the physician's orders; this was representative of much of his writings.

The US Canterbury v. Spence case established the principle of informed consent in US law. Earlier legal cases had created the underpinnings for informed consent, but his judgment gave a detailed and thought-through discourse on the matter. The judgment cites cases going back to 1914 as precedent for informed consent.

Infectious disease

In response to the bubonic plague, some city states restricted movement of people into them using cordon sanitaires, and separated those were suspected of being infected into makeshift camps. Merchant sailors were made to isolate in lazarettos, hospitals for infectious diseases. England created quarantine regulations in 1663 to confine ships suspected of being infected with the plague. In response to cholera outbreaks in the 1830s, some European cities people with symptoms were forced into lazarettos. An 1853 law in the United Kingdom made vaccination compulsory with those refusing to comply receiving fines. People with symptoms of tuberculosis have been detained in New York from 1902. During the Spanish flu pandemic western cities implemented social distancing and closed schools, churches, theatres and restricted public gatherings. During the COVID-19 pandemic many countries implemented lockdowns restricting movement, enforcing working from home and social distancing.

Mental health

In 1789 during the French Revolution, the French government issued a directive for the management of the insane. This directive ordered that the insane be incarcerated and treated. Bethlem Royal Hospital is a mental hospital in the United Kingdom, which started exclusively treating mental illness in 1377. In 1818, Urban Metcalf, a patient at Bethlam, published a book describing his experience there. He described physical restraint of patients who were to walls. This followed a report by the government in 1815 describing conditions in asylums in the UK.

Political use

Psychiatric diagnoses have been used for political purposes. Psychiatry can be used to bypass standard legal procedures and political incarceration. The use of hospitals instead of jails prevents those detainend from receiving legal aid, makes indefinite incarceration possible, discredits the individuals and their ideas. During the Nazi era and the Soviet rule religious and political dissenters were labeled as "mentally ill" and subjected to inhumane "treatments". From the 1960s to 1986, abuse of psychiatry for political and ideological purposes was reported to be systematic in the Soviet Union, and occasional in Eastern European countries such as Romania, Hungary, Czechoslovakia and Yugoslavia.

Legislative distinctions

Legislation may allow for involuntary of a particular disease or class of diseases such as mental disorders. Some countries have legislation to involuntarily detain or examine those suspected to have tuberculosis, or treat them if infected. Some countries have general legislation allowing for any treatment deemed necessary if an individual is unable to consent to a treatment due to lack of capacity.

Those treated for mental health disorders are committed before involuntary treatment. Those under community treatment orders (also known as outpatient commitment in some countries) may be ordered to take medication, and if they fail to may be committed and treated involuntarily.

In some countries, involuntary treatment for mental health is not used to treat a symptom that is present, but rather to reduce the risk of symptoms returning through the use prophylactic psychotropic medication. This is achieved through the use of outpatient commitment where a patient may be detained in hospital if they fail to take the medication doctors have prescribed them.

Forms

Standard modern day restraints

Chemical restraint, such as forcible injection with the antipsychotic haloperidol or benzodiazepine sedative midazolam, may be used to sedate a patient who is agitated. In some countries, antipsychotics and sedatives can be forcibly administered to those who are committed, using intramuscular depot injection. Those with anorexia nervosa may receive force-feeding.

Those with infectious diseases such as tuberculosis can be detained and isolated. Brazil, Bulgaria, Costa Rica, Croatia, Czechia, France, Hungary, Indonesia, Italy, Poland, and Russia make certain vaccinations mandatory.

In the Czech Republic, men convicted of sex offenses are in practice given the choice of long-term detention or castration. Japan requires transgender people to undergo sterilization to have their gender formally recognized.

Coercion in voluntary mental health treatment

Picture of a packet of cigarettes
Inducements such as access to cigarettes are used as leverage to encourage patients to accept treatment.

Individuals may be forced to undergo mental health treatment which is legally "voluntary" under the threat of involuntary treatment. Many individuals who legally would be viewed as receiving mental health treatment voluntarily believe that they have no choice in the matter.

Once voluntarily within a mental health hospital, rules, process, and information asymmetry (a healthcare providers know more how the hospital functions than a patient) can be used to achieve compliance from a person in voluntary treatment. To prevent someone from leaving voluntarily, staff may use stalling tactics made possible by the fact that all doors are locked. For example, the person may be referred to a member of staff who is rarely on the ward, or made to wait until after lunch or a meeting, behaving as if a person in voluntary treatment does not have the right to leave without permission. When the person is able to talk about leaving, the staff may use vague language to imply that the person is required to stay, relying on the fact that people in voluntary treatment do not understand their legal status.

Szmukler and Appelbaum constructed a hierarchy of types of coercion in mental health care, ranging from persuasion to interpersonal leverage, inducements, threats and compulsory treatment. Here persuasion refers to argument through reason. Forms of coercion that do not use legal compulsion are referred to as informal coercion or leverage. Interpersonal leverage may arise from the desire to please health workers with whom a relationship has formed. Threats may revolve around a health worker helping or hindering the receipt of government benefits. Studies show that 51%, 35% and 29% of mental health patients have experienced some form of informal coercion in the US, England and Switzerland respectively.

Non-voluntary treatment

In certain limited circumstance a patient may have capacity but be unable to consent to treatment at a time when a decision is necessary, in such cases surgery may be performed on a patient without consent. A patient may issue an advance healthcare directive specifying how they would like to be treated if they are unable to consent to treatment. In the UK, a healthcare worker does not need to follow an advanced directive but they will influence decisions. Alternatively, a surrogate decision-maker such as a relative, friend or healthcare professional may make decisions on a patients behalf if a patient is unable to.

Covert treatment

In some instances when a patients refuse the medication suggested by a healthcare professional healthcare workers will cause a patient to take the medication by hiding medication in their food a practice known as covert medication.

Competent adults

Picture of blood transfusion
Some Jehovah's Witnesses will choose to die rather than accept blood transfusions.

The faith of Jehovah's Witnesses forbids blood transfusion. Courts in the United States have consistently upheld the right of competent adults to decline blood transfusion even when it would be life-saving, though there have been exceptions where the death of a patient could leave a child orphaned.

In the United States, courts have ordered pregnant women to involuntarily undergo caesarean section, intrauterine transfusion, and enforced bed rest. There are cases of clinicians threatening pregnant patients with removal of child custody or withdrawal of care if they decline treatment. In the UK, courts are unable to force treatment on pregnant women who are deemed to have capacity, however as of 2016 there were no cases of a still pregnant woman being deemed to have capacity by a court.

Children

Picture of a child held while being vaccination
A child held while being vaccinated

Parents or medical doctors may make decision about the treatment of children, a principle known as parens patriae. In the United States, doctors are responsible for providing a good standard of care for patients who are children which can lead them to make decisions at odds with the parents wishes. Parents have less autonomy to make decisions about their children's care than adult patients have over their own care. Treatment may take place even if a child or adolescent disagree with treatment, though the wishes of a child patient are taken more into account the more burdensome treatment is and the worse the prognosis.

If a child does not assent to treatment they may be physically held while a procedure is carried out or anaesthesia is carried out. For some procedures, a child may be distracted to allow for treatment.

In Italy, court orders have been used to give children of Jehovah's Witnesses life-saving blood transfusion that were refused by their parents.

Prevalence

There is a lot of variation in the rate of involuntary commitment between countries. A review in Europe in 2004 found a thirty-fold difference in the rate of psychiatric commitment between countries, with the median rate being 74 per hundred thousand people. It is estimated that 38% of people who are involuntarily committed experience another form of compulsion such as seclusion or forced medication.

Effects

A 2014 Cochrane systematic review found that compulsory outpatient treatment of those with severe mental health disorders "results in no significant difference in service use, social functioning or quality of life compared with standard voluntary care." A 2006 review found that as many as 48% of respondents did not agree with their treatment, though a majority of people retrospectively agreed that involuntary medication had been in their best interest.

A review in 2011 looked at people's experience of coercion in mental health care. It found common themes of feeling violated, disrespected, and not being heard, commonly conceptualized as being dehumanized through isolation. A minority of narratives from people who had been treated involuntarily talked about the necessity of treatment in retrospect. Studies suggest that coercion in mental health care has a long-lasting psychological effect on individuals leading to reduced engagement and poorer social outcomes, but that this may be reduced by clinicians' knowledge of the effects of coercion.

Ethics

In medical ethics, involuntary treatment is conceptualized as a form of parens patriae whereby the state takes on the responsibilities of incompetent adults on the basis of the duty to protect and the duty of beneficence (the duty of the state to repair the random harms of nature). The duty to protect is reflected in utilitarianism and communitarianism philosophy, though psychiatrist Paul Chodoff asserted a responsibility to "chasten" this responsibility in light of the political abuse of psychiatry in the Soviet Union. This duty to protect has been criticized on the grounds that psychiatrists are not effective at predicting violence, and tend to overestimate the risk.

The obligatory dangerousness criterion is a principle that has been applied to some mental health law that holds that parens patriae should only be applied if an individual is a danger to themselves or others.

Paul Ricœur distinguishes two forms of self, the idem, a short term experience of the self, and the ipse, a longer term persistent experience of the self. In mental illness, the autonomy of the ipse can be undermined by the autonomy of the idem, so involuntary mental health treatment can trade one form of autonomy for another.

Sociology

Medical sociology seeks to understand the social processes underlying decisions made in medicine.

Sociologist Jeremy Dixon, speaking in the context of the United Kingdom, argues that assessment and monitoring of risk is a core part of mental health practice but that this risk is often in conflict with broadly-defined goals of recovery including living a satisfying life. He argues that this focus on risk causes mental health professionals to make decisions defensively based on reputational damage if there were to be any inquiry and that multidisciplinary approaches are used for this purpose. He cites research showing how mental health professionals may seek to shift the burden of responsibility onto individuals themselves (noting different clinical decisions for those with personality disorders compared to those with psychotic disorders because they are viewed as more responsible for their behaviours), or shift responsibility onto other public health services. Risk assessments themselves are rarely shared with patients.

Proponents and detractors

Protest graffiti against Involuntary treatment, Turin; TSO = MORTE means Involuntary treatment = Death

Proponents

Supporters of involuntary treatment include organizations such as the National Alliance on Mental Illness (NAMI), the American Psychiatric Association, and the Treatment Advocacy Center.

Detractors

A number of civil and human rights activists, anti-psychiatry groups, medical and academic organizations, researchers, and members of the psychiatric survivors movement vigorously oppose involuntary treatment on human rights grounds or on grounds of effectiveness and medical appropriateness, particularly with respect to involuntary administration of mind altering substances, ECT, and psychosurgery. Some criticism has been made regarding cost, as well as of conflicts of interest with the pharmaceutical industry. Critics, such as the New York Civil Liberties Union, have denounced the strong racial and socioeconomic biases in forced treatment orders.

Special rapporteurs of the United Nations (Catalina Devandas Aguilar and Dainius Puras) consider it as an infringement of the dignity of those subjected to it, with severe consequences for their physical and mental integrity and call on concerned states to put an end to respect individual's autonomy.

Part of André Franquin's poster for Amnesty International (1978)

Involuntary treatment is compared to torture on at least two special reports of the UN, one noting "forced psychiatric interventions, when committed against persons with psychosocial disabilities, satisfies both intent and purpose required under the article 1 of the Convention against Torture, notwithstanding claims of 'good intentions' by medical professionals." However, jurisdiction of some countries (e.g. France) requires intended harm (see: punitive psychiatry) to classify it as such and would classify involuntary treatment, rather as a degrading treatment, if recognize as it.

Amnesty international and Human Rights Watch oppose involuntary treatment.

Laws internationally

United States

Mentally competent patients have a general right to refuse medical treatment.

All states in the U.S. allow for some form of involuntary treatment for mental illness or erratic behavior for short periods of time under emergency conditions, although criteria vary. Further involuntary treatment outside clear and pressing emergencies where there is asserted to be a threat to public safety usually requires a court order, and all states currently have some process in place to allow this. Since the late 1990s, a growing number of states have adopted Assisted Outpatient Commitment (AOC) laws.

Under assisted outpatient commitment, people committed involuntarily can live outside the psychiatric hospital, sometimes under strict conditions including reporting to mandatory psychiatric appointments, taking psychiatric drugs in the presence of a nursing team, and testing medication blood levels. Forty-five states presently allow for outpatient commitment.

In 1975, the U.S. Supreme Court ruled in O'Connor v. Donaldson that involuntary hospitalization and/or treatment violates an individual's civil rights. The individual must be exhibiting behavior that is a danger to themselves or others and a court order must be received for more than a short (e.g. 72-hour) detention. The treatment must take place in the least restrictive setting possible. This ruling has since been watered down through jurisprudence in some respects and strengthened in other respects. Long term "warehousing", through de-institutionalization, declined in the following years, though the number of people receiving involuntary treatment has increased more recently. The statutes vary somewhat from state to state.

In 1979, the United States Court of Appeals for the First Circuit established in Rogers v. Okin that a competent person committed to a psychiatric hospital has the right to refuse treatment in non-emergency situations. The case of Rennie v. Klein established that an involuntarily committed individual has a constitutional right to refuse psychotropic medication without a court order. Rogers v. Okin established the person's right to make treatment decisions so long as they are still presumed competent.

Additional U.S. Supreme Court decisions have added more restraints, and some expansions or effective sanctioning, to involuntary commitment and treatment. Foucha v. Louisiana established the unconstitutionality of the continued commitment of an insanity acquittee who was not suffering from a mental illness. In Jackson v. Indiana the court ruled that a person adjudicated incompetent could not be indefinitely committed. In Perry v. Louisiana the court struck down the forcible medication of a prisoner for the purposes of rendering him competent to be executed. In Riggins v. Nevada the court ruled that a defendant had the right to refuse psychiatric medication while he was on trial, given to mitigate his psychiatric symptoms. Sell v. United States imposed stringent limits on the right of a lower court to order the forcible administration of antipsychotic medication to a criminal defendant who had been determined to be incompetent to stand trial for the sole purpose of making them competent and able to be tried. In Washington v. Harper the Supreme Court upheld the involuntary medication of correctional facility inmates only under certain conditions as determined by established policy and procedures.

Europe

Globe icon.
The examples and perspective in this section deal primarily with the Western world and do not represent a worldwide view of the subject. You may improve this section, discuss the issue on the talk page, or create a new section, as appropriate. (July 2020) (Learn how and when to remove this template message)
Country During involuntary commitment During outpatient commitment (community treatment order)
France  Yes Yes
UK  Yes Yes (after being recalled to hospital)
Germany  Yes Yes
Switzerland
No in Geneva, not specified or yes for other cantons
Italy  Yes Yes (7 days renewable)
Austria  (no neuroleptic depot injection) (no neuroleptic depot injection)
The Netherlands  Yes (law passed recently) Yes (at home)
Ireland
no outpatient commitment

Word-sense disambiguation

From Wikipedia, the free encyclopedia

Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning.

Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, and completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.

Accuracy of current algorithms is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90% (as of 2009), with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.

Variants

Disambiguation requires two strict inputs: a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated (in some methods, a training corpus of language examples is also required). WSD task has two variants: "lexical sample" (disambiguating the occurrences of a small sample of target words which were previously selected) and "all words" task (disambiguation of all the words in a running text). "All words" task is generally considered a more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word.

History

WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics. Warren Weaver first introduced the problem in a computational context in his 1949 memorandum on translation. Later, Bar-Hillel (1960) argued that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge.

In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, starting with Wilks' preference semantics. However, since WSD systems were at the time largely rule-based and hand-coded they were prone to a knowledge acquisition bottleneck.

By the 1980s large-scale lexical resources, such as the Oxford Advanced Learner's Dictionary of Current English (OALD), became available: hand-coding was replaced with knowledge automatically extracted from these resources, but disambiguation was still knowledge-based or dictionary-based.

In the 1990s, the statistical revolution advanced computational linguistics, and WSD became a paradigm problem on which to apply supervised machine learning techniques.

The 2000s saw supervised techniques reach a plateau in accuracy, and so attention has shifted to coarser-grained senses, domain adaptation, semi-supervised and unsupervised corpus-based systems, combinations of different methods, and the return of knowledge-based systems via graph-based methods. Still, supervised systems continue to perform best.

Difficulties

Differences between dictionaries

One problem with word sense disambiguation is deciding what the senses are, as different dictionaries and thesauruses will provide different divisions of words into senses. Some researchers have suggested choosing a particular dictionary, and using its set of senses to deal with this issue. Generally, however, research results using broad distinctions in senses have been much better than those using narrow ones. Most researchers continue to work on fine-grained WSD.

Most research in the field of WSD is performed by using WordNet as a reference sense inventory for English. WordNet is a computational lexicon that encodes concepts as synonym sets (e.g. the concept of car is encoded as { car, auto, automobile, machine, motorcar }). Other resources used for disambiguation purposes include Roget's Thesaurus and Wikipedia. More recently, BabelNet, a multilingual encyclopedic dictionary, has been used for multilingual WSD.

Part-of-speech tagging

In any real test, part-of-speech tagging and sense tagging have proven to be very closely related, with each potentially imposing constraints upon the other. The question whether these tasks should be kept together or decoupled is still not unanimously resolved, but recently scientists incline to test these things separately (e.g. in the Senseval/SemEval competitions parts of speech are provided as input for the text to disambiguate).

Both WSD and part-of-speech tagging involve disambiguating or tagging with words. However, algorithms used for one do not tend to work well for the other, mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words, whereas the sense of a word may be determined by words further away. The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96% accuracy or better, as compared to less than 75%[citation needed] accuracy in word sense disambiguation with supervised learning. These figures are typical for English, and may be very different from those for other languages.

Inter-judge variance

Another problem is inter-judge variance. WSD systems are normally tested by having their results on a task compared against those of a human. However, while it is relatively easy to assign parts of speech to text, training people to tag senses has been proven to be far more difficult. While users can memorize all of the possible parts of speech a word can take, it is often impossible for individuals to memorize all of the senses a word can take. Moreover, humans do not agree on the task at hand – give a list of senses and sentences, and humans will not always agree on which word belongs in which sense.

As human performance serves as the standard, it is an upper bound for computer performance. Human performance, however, is much better on coarse-grained than fine-grained distinctions, so this again is why research on coarse-grained distinctions has been put to test in recent WSD evaluation exercises.

Sense inventory and algorithms' task-dependency

A task-independent sense inventory is not a coherent concept: each task requires its own division of word meaning into senses relevant to the task. Additionally, completely different algorithms might be required by different applications. In machine translation, the problem takes the form of target word selection. The "senses" are words in the target language, which often correspond to significant meaning distinctions in the source language ("bank" could translate to the French "banque"—that is, 'financial bank' or "rive"—that is, 'edge of river'). In information retrieval, a sense inventory is not necessarily required, because it is enough to know that a word is used in the same sense in the query and a retrieved document; what sense that is, is unimportant.

Discreteness of senses

Finally, the very notion of "word sense" is slippery and controversial. Most people can agree in distinctions at the coarse-grained homograph level (e.g., pen as writing instrument or enclosure), but go down one level to fine-grained polysemy, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences. Word meaning is in principle infinitely variable and context-sensitive. It does not divide up easily into distinct or discrete sub-meanings. Lexicographers frequently discover in corpora loose and overlapping word meanings, and standard or conventional meanings extended, modulated, and exploited in a bewildering variety of ways. The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a word, making it seem like words are well-behaved semantically. However, it is not at all clear if these same meaning distinctions are applicable in computational applications, as the decisions of lexicographers are usually driven by other considerations. In 2009, a task – named lexical substitution – was proposed as a possible solution to the sense discreteness problem. The task consists of providing a substitute for a word in context that preserves the meaning of the original word (potentially, substitutes can be chosen from the full lexicon of the target language, thus overcoming discreteness).

Approaches and methods

There are two main approaches to WSD – deep approaches and shallow approaches.

Deep approaches presume access to a comprehensive body of world knowledge. These approaches are generally not considered to be very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside very limited domains. Additionally due to the long tradition in computational linguistics, of trying such approaches in terms of coded knowledge and in some cases, it can be hard to distinguish between knowledge involved in linguistic or world knowledge. The first attempt was that by Margaret Masterman and her colleagues, at the Cambridge Language Research Unit in England, in the 1950s. This attempt used as data a punched-card version of Roget's Thesaurus and its numbered "heads", as an indicator of topics and looked for repetitions in text, using a set intersection algorithm. It was not very successful, but had strong relationships to later work, especially Yarowsky's machine learning optimisation of a thesaurus method in the 1990s.

Shallow approaches don't try to understand the text, but instead consider the surrounding words. These rules can be automatically derived by the computer, using a training corpus of words tagged with their word senses. This approach, while theoretically not as powerful as deep approaches, gives superior results in practice, due to the computer's limited world knowledge.

There are four conventional approaches to WSD:

Almost all these approaches work by defining a window of n content words around each word to be disambiguated in the corpus, and statistically analyzing those n surrounding words. Two shallow approaches used to train and then disambiguate are Naïve Bayes classifiers and decision trees. In recent research, kernel-based methods such as support vector machines have shown superior performance in supervised learning. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art.

Dictionary- and knowledge-based methods

The Lesk algorithm is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary). A similar approach searches for the shortest path between two words: the second word is iteratively searched among the definitions of every semantic variant of the first word, then among the definitions of every semantic variant of each word in the previous definitions and so on. Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word.

An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as WordNet. Graph-based methods reminiscent of spreading activation research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods or even outperforming them on specific domains. Recently, it has been reported that simple graph connectivity measures, such as degree, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base. Also, automatically transferring knowledge in the form of semantic relations from Wikipedia to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).

Supervised methods

Supervised methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence, common sense and reasoning are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as feature selection, parameter optimization, and ensemble learning. Support Vector Machines and memory-based learning have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged corpora for training, which are laborious and expensive to create.

Semi-supervised methods

Because of the lack of training data, many word sense disambiguation algorithms use semi-supervised learning, which allows both labeled and unlabeled data. The Yarowsky algorithm was an early example of such an algorithm. It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation.

The bootstrapping approach starts from a small amount of seed data for each word: either manually tagged training examples or a small number of surefire decision rules (e.g., 'play' in the context of 'bass' almost always indicates the musical instrument). The seeds are used to train an initial classifier, using any supervised method. This classifier is then used on the untagged portion of the corpus to extract a larger training set, in which only the most confident classifications are included. The process repeats, each new classifier being trained on a successively larger training corpus, until the whole corpus is consumed, or until a given maximum number of iterations is reached.

Other semi-supervised techniques use large quantities of untagged corpora to provide co-occurrence information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains.

Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned bilingual corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system.

Unsupervised methods

Unsupervised learning is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context, a task referred to as word sense induction or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than for the other methods described above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a mapping to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web search result clustering by increasing the quality of result clusters and the degree diversification of result lists. It is hoped that unsupervised learning will overcome the knowledge acquisition bottleneck because they are not dependent on manual effort.

Representing words considering their context through fixed-size dense vectors (word embeddings) has become one of the most fundamental blocks in several NLP systems. Even though most of traditional word-embedding techniques conflate words with multiple meanings into a single vector representation, they still can be used to improve WSD. A simple approach to employ pre-computed word embeddings to represent word senses is to compute the centroids of sense clusters. In addition to word-embedding techniques, lexical databases (e.g., WordNet, ConceptNet, BabelNet) can also assist unsupervised systems in mapping words and their senses as dictionaries. Some techniques that combine lexical databases and word embeddings are presented in AutoExtend and Most Suitable Sense Annotation (MSSA). In AutoExtend, they present a method that decouples an object input representation into its properties, such as words and their word senses. AutoExtend uses a graph structure to map words (e.g. text) and non-word (e.g. synsets in WordNet) objects as nodes and the relationship between nodes as edges. The relations (edges) in AutoExtend can either express the addition or similarity between its nodes. The former captures the intuition behind the offset calculus, while the latter defines the similarity between two nodes. In MSSA, an unsupervised disambiguation system uses the similarity between word senses in a fixed context window to select the most suitable word sense using a pre-trained word-embedding model and WordNet. For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target word to its immediately adjacent neighbors (i.e., predecessor and successor words). After all words are annotated and disambiguated, they can be used as a training corpus in any standard word-embedding technique. In its improved version, MSSA can make use of word sense embeddings to repeat its disambiguation process iteratively.

Other approaches

Other approaches may vary differently in their methods:

  • Domain-driven disambiguation;
  • Identification of dominant word senses;
  • WSD using Cross-Lingual Evidence.
  • WSD solution in John Ball's language independent NLU combining Patom Theory and RRG (Role and Reference Grammar)
  • Type inference in constraint-based grammars

Other languages

  • Hindi : Lack of lexical resources in Hindi have hindered the performance of supervised models of WSD, while the unsupervised models suffer due to extensive morphology. A possible solution to this problem is the design of a WSD model by means of parallel corpora. The creation of the Hindi WordNet has paved way for several Supervised methods which have been proven to produce a higher accuracy in disambiguating nouns.

Local impediments and summary

The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem. Unsupervised methods rely on knowledge about word senses, which is only sparsely formulated in dictionaries and lexical databases. Supervised methods depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the Senseval exercises.

One of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically. WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval (IR). In this case, however, the reverse is also true: web search engines implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD. The historic lack of training data has provoked the appearance of some new algorithms and techniques, as described in Automatic acquisition of sense-tagged corpora.

External knowledge sources

Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They can be classified as follows:

Structured:

  1. Machine-readable dictionaries (MRDs)
  2. Ontologies
  3. Thesauri

Unstructured:

  1. Collocation resources
  2. Other resources (such as word frequency lists, stoplists, domain labels, etc.)
  3. Corpora: raw corpora and sense-annotated corpora

Evaluation

Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale, data sets. In order to test one's algorithm, developers should spend their time to annotate all word occurrences. And comparing methods even on the same corpus is not eligible if there is different sense inventories.

In order to define common evaluation datasets and procedures, public evaluation campaigns have been organized. Senseval (now renamed SemEval) is an international word sense disambiguation competition, held every three years since 1998: Senseval-1 (1998), Senseval-2 (2001), Senseval-3 (2004), and its successor, SemEval (2007). The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as semantic role labeling, gloss WSD, lexical substitution, etc. The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods (especially for avoiding bad performance in lack of training examples).

In recent years 2007-2012, the WSD evaluation task choices had grown and the criterion for evaluating WSD has changed drastically depending on the variant of the WSD evaluation task. Below enumerates the variety of WSD tasks:

Task design choices

As technology evolves, the Word Sense Disambiguation (WSD) tasks grows in different flavors towards various research directions and for more languages:

  • Classic monolingual WSD evaluation tasks use WordNet as the sense inventory and are largely based on supervised/semi-supervised classification with the manually sense annotated corpora:
    • Classic English WSD uses the Princeton WordNet as it sense inventory and the primary classification input is normally based on the SemCor corpus.
    • Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged in their respective languages. Often researchers will also tapped on the SemCor corpus and aligned bitexts with English as its source language
  • Cross-lingual WSD evaluation task is also focused on WSD across 2 or more languages simultaneously. Unlike the Multilingual WSD tasks, instead of providing manually sense-annotated examples for each sense of a polysemous noun, the sense inventory is built up on the basis of parallel corpora, e.g. Europarl corpus.
  • Multilingual WSD evaluation tasks focused on WSD across 2 or more languages simultaneously, using their respective WordNets as its sense inventories or BabelNet as multilingual sense inventory. It evolved from the Translation WSD evaluation tasks that took place in Senseval-2. A popular approach is to carry out monolingual WSD and then map the source language senses into the corresponding target word translations.
  • Word Sense Induction and Disambiguation task is a combined task evaluation where the sense inventory is first induced from a fixed training set data, consisting of polysemous words and the sentence that they occurred in, then WSD is performed on a different testing data set.

Software

  • Babelfy, a unified state-of-the-art system for multilingual Word Sense Disambiguation and Entity Linking
  • BabelNet API, a Java API for knowledge-based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet semantic network
  • WordNet::SenseRelate, a project that includes free, open source systems for word sense disambiguation and lexical sample sense disambiguation
  • UKB: Graph Base WSD, a collection of programs for performing graph-based Word Sense Disambiguation and lexical similarity/relatedness using a pre-existing Lexical Knowledge Base
  • pyWSD, python implementations of Word Sense Disambiguation (WSD) technologies

Grammatical mood

From Wikipedia, the free encyclopedia
 
In linguistics, grammatical mood is a grammatical feature of verbs, used for signaling modality. That is, it is the use of verbal inflections that allow speakers to express their attitude toward what they are saying (for example, a statement of fact, of desire, of command, etc.). The term is also used more broadly to describe the syntactic expression of modality – that is, the use of verb phrases that do not involve inflection of the verb itself.

Mood is distinct from grammatical tense or grammatical aspect, although the same word patterns are used for expressing more than one of these meanings at the same time in many languages, including English and most other modern Indo-European languages. (See tense–aspect–mood for a discussion of this.)

Some examples of moods are indicative, interrogative, imperative, subjunctive, injunctive, optative, and potential. These are all finite forms of the verb. Infinitives, gerunds, and participles, which are non-finite forms of the verb, are not considered to be examples of moods.

Some Uralic Samoyedic languages have more than ten moods; Nenets has as many as sixteen. The original Indo-European inventory of moods consisted of indicative, subjunctive, optative, and imperative. Not every Indo-European language has all of these moods, but the most conservative ones such as Avestan, Ancient Greek, and Vedic Sanskrit have them all. English has indicative, imperative, and subjunctive moods; other moods, such as the conditional, do not appear as morphologically distinct forms.

Not all the moods listed below are clearly conceptually distinct. Individual terminology varies from language to language, and the coverage of, for example, the "conditional" mood in one language may largely overlap with that of the "hypothetical" or "potential" mood in another. Even when two different moods exist in the same language, their respective usages may blur, or may be defined by syntactic rather than semantic criteria. For example, the subjunctive and optative moods in Ancient Greek alternate syntactically in many subordinate clauses, depending on the tense of the main verb. The usage of the indicative, subjunctive, and jussive moods in Classical Arabic is almost completely controlled by syntactic context. The only possible alternation in the same context is between indicative and jussive following the negative particle .

Realis moods

Realis moods are a category of grammatical moods that indicate that something is actually the case or actually not the case. The most common realis mood is the indicative mood. Some languages have a distinct generic mood for expressing general truths.

Indicative

The indicative mood, or evidential mood, is used for factual statements and positive beliefs. It is the mood of reality. The indicative mood is the most commonly used mood and is found in all languages. Example: "Paul is eating an apple" or "John eats apples".

Irrealis moods

Irrealis moods or non indicative moods are the set of grammatical moods that indicate that something is not actually the case or a certain situation or action is not known to have happened. They are any verb or sentence mood that is not a realis mood. They may be part of expressions of necessity, possibility, requirement, wish or desire, fear, or as part of counterfactual reasoning, etc.

Irrealis verb forms are used when speaking of an event which has not happened, is not likely to happen, or is otherwise far removed from the real course of events. For example, in the sentence "If you had done your homework, you wouldn't have failed the class", had done is an irrealis verb form.

Some languages have distinct irrealis grammatical verb forms. Many Indo-European languages preserve a subjunctive mood. Some also preserve an optative mood that describes events that are wished for or hoped for but not factual.

Common irrealis moods are the conditional, the subjunctive, the optative, the jussive, and the potential. For other examples, see the main article for each respective mood.

Subjunctive

The subjunctive mood, sometimes called conjunctive mood, has several uses in dependent clauses. Examples include discussing imaginary or hypothetical events and situations, expressing opinions or emotions, or making polite requests (the exact scope is language-specific). A subjunctive mood exists in English, though it is not an inflectional form of the verb but rather a clause type which uses the bare form of the verb also used in imperatives, infinitives, and other constructions. An example of the English subjunctive is "Jill suggested that Paul take his medicine", as opposed to the indicative sentence "Jill believes that Paul takes his medicine".

Other uses of the subjunctive in English are archaisms, as in "And if he be not able to bring a lamb, then he shall bring for his trespass..." (KJV Leviticus 5:7). Statements such as "I will ensure that he leave immediately" often sound archaic or formal, and have been largely supplanted by constructions with the indicative, like "I will ensure that he leaves immediately".

Some Germanic languages distinguish between two types of subjunctive moods, for example, the Konjunktiv I and II in German.

Subjunctive version of "John eats if he is hungry." (subjunctive part in bold)
Language Sentence
English John would eat if he were hungry.
Danish John ville spise, hvis han var sulten.
Dutch Jan zou eten, als hij hongerig zou zijn.
French1 Jean mangerait s’il eût faim.
German Johannes äße, wenn er hungrig wäre.
Hindi जॉन खाता अगर उसे भूख होती

jôn khātā agar use bhūkh hotī.

Italian Giovanni mangerebbe se avesse fame.
Latvian Jānis ēstu, ja būtu izsalcis.
Polish Jan jadłby, gdyby zgłodniał.
Portuguese O João comeria se tivesse fome.
Russian Иван поел бы, если бы был голоден.
Spanish Juan comería si tuviera hambre.
Swedish Johan skulle äta, om han vore hungrig.
Slovenian Janez bi jedel, če bi bil lačen.

1 In modern usage, the imperfect indicative usually replaces the imperfect subjunctive in this type of sentence.

The subjunctive mood figures prominently in the grammar of the Romance languages, which require this mood for certain types of dependent clauses. This point commonly causes difficulty for English speakers learning these languages.

In certain other languages, the dubitative or the conditional moods may be employed instead of the subjunctive in referring to doubtful or unlikely events (see the main article).

Conditional

The conditional mood is used for speaking of an event whose realization is dependent upon another condition, particularly, but not exclusively, in conditional sentences. In Modern English, this type of modality is expressed via a periphrastic construction, with the form would + infinitive, (for example, I would buy), and thus is a mood only in the broad sense and not in the more common narrow sense of the term "mood" requiring morphological changes in the verb. In other languages, verbs have a specific conditional inflection. In German, the conditional mood is identical to one of the two subjunctive moods (Konjunktiv II, see above).

Conditional version of "John eats if he is hungry." (conditional part in bold)
English John would eat if he were hungry.
Basque Jonek jango luke, goserik balu.
Estonian Juhan sööks, kui tal oleks nälg
Finnish Juha söisi, jos hänellä olisi nälkä
French Jean mangerait s'il avait faim.
German Johannes äße, wenn er hungrig wäre.

Also: Johannes würde essen, wenn er hungrig wäre.

Hindi जॉन खाता अगर उसे भूख होती।

jôn khātā agar usē bhūkh hotī.

Irish D'itheadh Seán dá mbeadh ocras air.
Italian Giovanni mangerebbe se avesse fame.
Latvian Jānis ēstu, ja būtu izsalcis.
Polish Jan jadłby, gdyby zgłodniał.
Portuguese João comeria se estivesse com fome.
Russian Иван поел бы, если бы был голоден.
Spanish Juan comería si tuviera hambre.
Swedish Johan skulle äta, om han vore hungrig.

In the Romance languages, the conditional form is used primarily in the apodosis (main clause) of conditional sentences, and in a few set phrases where it expresses courtesy or doubt. The main verb in the protasis (dependent clause) is usually in the subjunctive or in the indicative mood. However, this is not a universal trait and among others in German (as above), Finnish, and Romanian (even though the last is a Romance language), the conditional mood is used in both the apodosis and the protasis. A further example is a sentence "I would buy a house if I earned a lot of money".

  • Irish has conditional marking in both clauses, -adh in the verbs illustrated: d'itheadh 'would eat, would have eaten' and beadh 'would be, would have been', along with a specific irrealis conditional 'if', which contrasts with the realis conditional 'if' (i.e. Ithfidh sé má bhíonn ocras air. 'He'll eat if he is hungry').
  • In Finnish, both clauses likewise have the conditional marker -isi-: Ostaisin talon, jos ansaitsisin paljon rahaa.
  • In Polish (as well as in eastern and other western Slavic languages), the conditional marker -by also appears twice: Kupiłbym dom, gdybym zarabiał dużo pieniędzy.
  • In Hindi, the conditional markers -ता (tā), -ती (tī), -ते (te) and -तीं (tī̃) (agreeing in gender and number with the subject and the direct object) comes twice: मैं घर खरीदता अगर बहौत पैसे कमाता। (maiṁ ghar kharīda agar bahaut paisē kamā). The conditional (or contrafactual) form in Hindi corresponds to perfect conditional of Romance and the Germanic languages. So, the sentence literally translate to "I would have bought a house if I earned a lot of money." 

Because English is used as a lingua franca, a common error among second-language speakers is to use "would" in both clauses. For example, *"I would buy if I would earn...".

Optative

The optative mood expresses hopes, wishes or commands and has other uses that may overlap with the subjunctive mood. Few languages have an optative as a distinct mood; some that do are Albanian, Ancient Greek, Kazakh, Japanese, Finnish, Nepali, and Sanskrit.

Imperative

The imperative mood expresses direct commands, prohibitions, and requests. In many circumstances, using the imperative mood may sound blunt or even rude, so it is often used with care. Example: "Pat, do your homework now". An imperative is used for telling someone to do something without argument. Many languages, including English, use the bare verb stem to form the imperative (such as "go", "run", "do"). Other languages, such as Seri, Hindi, and Latin, however, use special imperative forms.

  • In English, the second person is implied by the imperative except when first-person plural is specified, as in "Let's go" ("Let us go").
  • In Romance languages, a first person plural exists in the imperative mood: Spanish: Vayamos a la playa; French: Allons à la plage (both meaning: Let's go to the beach).
  • In Hindi, imperatives can be put into the present and the future tense. Imperative forms of Hindi verb karnā (to do) is shown in the table belowː
2nd

Person

Formality Present Future
Intimate kar kariyo
Familiar tum karo kar
Formal āp kariye kariyegā

The prohibitive mood, the negative imperative, may be grammatically or morphologically different from the imperative mood in some languages. It indicates that the action of the verb is not permitted. For example, "Don't you go!"

In English, the imperative is sometimes used for forming a conditional sentence: for example, "go eastwards a mile, and you'll see it" means "if you go eastwards a mile, you will see it".

Imperative version of "John does his homework."
English John, do your homework!
French Jean, fais tes devoirs !
German Johannes, mach deine Hausaufgaben!
Russian Иван, делай домашнее задание!

Jussive

The jussive, similarly to the imperative, expresses orders, commands, exhortations, but particularly to a third person not present. An imperative, in contrast, generally applies to the listener. When a language is said to have a jussive, the jussive forms are different from the imperative ones, but may be the same as the forms called "subjunctive" in that language. Latin and Hindi are examples of where the jussive is simply about certain specific uses of the subjunctive. Arabic, however, is a language with distinct subjunctive, imperative, and jussive conjugations.

Potential

The potential mood is a mood of probability indicating that, in the opinion of the speaker, the action or occurrence is considered likely. It is used in Finnish, Japanese, in Sanskrit (where the so-called optative mood can serve equally well as a potential mood), in Northern Wu, and in the Sami languages. (In Japanese, it is often called something like tentative, since potential is used for referring to a voice indicating capability to perform the action.)

In Finnish, it is mostly a literary device, as it has virtually disappeared from daily spoken language in most dialects. Its affix is -ne-, as in *men + ne + emennee "(she/he/it) will probably go".

In Hungarian, the potential is formed by the suffix -hat/-het and it can express both possibility and permission: adhat "may give, can give"; Mehetünk? "Can we go?"

In English, it is formed by means of the auxiliaries may, can, ought, and must: "She may go."

Presumptive

The presumptive mood is used to express presupposition or hypothesis, regardless of the fact denoted by the verb, as well as other more or less similar attitudes: doubt, curiosity, concern, condition, indifference, and inevitability. It is used in Romanian, Hindi, Gujarati, and Punjabi.

In Romanian, the presumptive mood conjugations of the verb vrea are used with the infinitive form of verbs. The present tense and the past tense infinitives are respectively used to form the present and the past tense of the presumptive mood.

In Hindi, the presumptive mood conjugations of the verb honā (to be) are used with the perfective, habitual, and progressive aspectual participles to form the perfective presumptive, habitual presumptive, and the progressive presumptive moods. The same presumptive mood conjugations are used for present, future, and past tenses.

Presumptive Mood Conjugations
Person Singular Plural
1st 2nd 3rd 1st 2nd 3rd
Romanian oi o om oți or
Hindi hūṁgā hogā hoṁgē hogē hoṁgē
hūṁgī hogī hoṁgī hogī hoṁgī

Tense Sentence Translation
Romanian Present tu oi face You might do.
Past tu oi fi făcut You might have done.
Progressive tu oi fi făcând You might be doing.

Aspect Tense Sentence Translation
Hindi Habitual Present tū kartā hoga abhī You must/might be doing it now.
Past tū kartā hogā pêhlē. You must/might have done it before (habitually in the past).
Perfective Present tūnē kiyā hogā abhī. You must/might have done now.
Past tūnē kiyā hogā pêhlē. You must/might have done it before (in the past).
Progressive Present tū kar rahā hogā abhī You must/might be doing it now.
Past tū kar rahā hogā do din pêhlē You must/might have been doing it two days ago.
Future tū kar rahā hogā do din bād You must/might be doing it two days from now.

Note:

  1. The translations are just the closest possible English approximations and not exact.
  2. Only masculine conjugations are shown for Hindi.

Hypothetical

A few languages use a hypothetical mood, which is used in sentences such as "you could have cut yourself", representing something that might have happened but did not.

Inferential

The inferential mood is used to report unwitnessed events without confirming them. Often, there is no doubt as to the veracity of the statement (for example, if it were on the news), but simply the fact that the speaker was not personally present at the event forces them to use this mood.

In the Balkan languages, the same forms used for the inferential mood also function as admiratives. When referring to Balkan languages, it is often called renarrative mood; when referring to Estonian, it is called oblique mood.

The inferential is usually impossible to be distinguishably translated into English. For instance, indicative Bulgarian той отиде (toy otide) and Turkish o gitti will be translated the same as inferential той отишъл (toy otishal) and o gitmiş — with the English indicative he went. Using the first pair, however, implies very strongly that the speaker either witnessed the event or is very sure that it took place. The second pair implies either that the speaker did not in fact witness it take place, that it occurred in the remote past or that there is considerable doubt as to whether it actually happened. If it were necessary to make the distinction, then the English constructions "he must have gone" or "he is said to have gone" would partly translate the inferential.

Interrogative

The interrogative (or interrogatory) mood is used for asking questions. Most languages do not have a special mood for asking questions, but exceptions include Welsh, Nenets, and Eskimo languages such as Greenlandic.

Deontic mood vs. epistemic mood

Linguists also differentiate moods into two parental irrealis categories: deontic mood and epistemic mood. Deontic mood describes whether one could or should be able to do something. An example of deontic mood is: She should/may start. On the other hand, epistemic mood describes the chance or possibility of something happening. This would then change our example to: She may have started. To further explain modality, linguists introduce weak mood. A weak deontic mood describes how a course of action is not recommended or is frowned upon. A weak epistemic mood includes the terms "perhaps" and "possibly".

Moods in Oceanic languages

Pingelapese

Pingelapese is a Micronesian language spoken on the Pingelap atoll and on two of the eastern Caroline Islands, called the high island of Pohnpei. e and ae are auxiliary verbs found in Pingelapese. Though seemingly interchangeable, e and ae are separate phonemes and have different uses. A Pingelapese speaker would choose to use e when they have a high degree of certainty in what they are saying and ae when they are less certain. This therefore illustrates that e and ae are mood indicators. They have no effect on the direct translation of a sentence, but they are used to alter the mood of the sentence spoken. The following example shows the difference between e and ae when applied in the same sentence.

Ngaei rong pwa Soahn e laid.

‘I heard that John was fishing (I am certain about it).’

Ngaei rong pwa Soahn ae laid.

‘I heard that John was fishing (but I am not certain about it).’

The use of ae instead of e can also indicate an interrogative sentence. This is a form of non-declarative speech that demonstrates the speaker has no commitment to the statement they are saying. The following sentence is an example.

Soahn ae laid?

‘Does John fish?’

Reo Rapa

The language we know as Reo Rapa was created as a result of the introduction of Tahitian to the Rapa monolingual community. Old Rapa words are still used for the grammar and structure of the sentence or phrase, but most common content words were replaced with Tahitian. The Reo Rapa language uses Tense–Aspect–Mood (TAM) in their sentence structure such as the imperfective TAM marker /e/ and the imperative TAM marker /a/.

For example:

  • e hina’aro na vau tō mei’a ra
    • e (Imperfective TAM marker) + hina’aro (Like) + na (Deixis) + vau (Singular) + (Definite) + mei’a (Banana) ra (Deixis)
      • 'I would like those bananas (you mentioned).'

Mortlockese

Mortlockese is an Austronesian language made up of eleven dialects over the eleven atolls that make up the Mortlock Islands in Micronesia. Various TAM markers are used in the language. Mood markers include the past tense hortative (marking encouragement or to urge) aa, the hortative which denotes a polite tone, min or tin to stress the importance of something, and the word to denote warning or caution. Each of these markers is used in conjunction with the subject proclitics except for the aa marker.

Operator (computer programming)

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Operator_(computer_programmin...