Textual entailment
Encyclopedia
Textual entailment in natural language processing
is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. Textual entailment is not the same as pure logical entailment
- it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain.
mapping between language expressions and meanings. To interpret a text correctly, would in theory require a thorough semantic interpretation into a logic-based representation of its meanings. Practical solutions voor natural language processing seek to go not that deep and use textual entailment in a more shallow way.
An example of a positive TE (text entails hypothesis) is:
An example of a negative TE (text contradicts hypothesis) is:
An example of a non-TE (text does not entail nor contradict) is:
(QA), Information Extraction
(IE), (multi-document)
summarization
and machine translation
(MT) evaluation, need a model for this variability phenomenon in order to recognize that a particular target meaning can be inferred from different text variants. In 2004 Recognizing Textual Entailment (RTE) has been proposed as a generic task that captures major semantic inference needs across many natural language processing applications.
Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarity of the texts involved.
Natural language processing
Natural language processing is a field of computer science and linguistics concerned with the interactions between computers and human languages; it began as a branch of artificial intelligence....
is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. Textual entailment is not the same as pure logical entailment
Entailment
In logic, entailment is a relation between a set of sentences and a sentence. Let Γ be a set of one or more sentences; let S1 be the conjunction of the elements of Γ, and let S2 be a sentence: then, Γ entails S2 if and only if S1 and not-S2 are logically inconsistent...
- it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain.
Ambiguity of natural language
A characteristic of natural language is that are many different way to state what you want to say: several meanings can be contained in a single text and that the same meaning can be expressed by different texts. This variability of semantic expression can be seen as the dual problem of language ambiguity. Together they result in a many-to-manyMany-to-many (data model)
In systems analysis, a many-to-many relationship is a type of cardinality that refers to the relationship between two entities A and B in which A may contain a parent row for which there are many children in B and vice versa. For instance, think of A as Authors, and B as Books...
mapping between language expressions and meanings. To interpret a text correctly, would in theory require a thorough semantic interpretation into a logic-based representation of its meanings. Practical solutions voor natural language processing seek to go not that deep and use textual entailment in a more shallow way.
Examples
Textual entailment can be illustrated with examples of three different relations:An example of a positive TE (text entails hypothesis) is:
- text: If you help the needy, God will reward you.
- hypothesis: Giving money to a poor man has good consequences.
An example of a negative TE (text contradicts hypothesis) is:
- text: If you help the needy, God will reward you.
- hypothesis: Giving money to a poor man has no consequences.
An example of a non-TE (text does not entail nor contradict) is:
- text: If you help the needy, God will reward you.
- hypothesis: Giving money to a poor man will make you better person.
Recognizing textual entailment
Many natural language processing applications, like Question AnsweringQuestion answering
In information retrieval and natural language processing , question answering is the task of automatically answering a question posed in natural language...
(QA), Information Extraction
Information extraction
Information extraction is a type of information retrieval whose goal is to automatically extract structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language...
(IE), (multi-document)
Multi-document summarization
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Resulting summary report allows individual users, so as professional information consumers, to quickly familiarize themselves with information contained in a...
summarization
Automatic summarization
Automatic summarization is the creation of a shortened version of a text by a computer program. The product of this procedure still contains the most important points of the original text....
and machine translation
Machine translation
Machine translation, sometimes referred to by the abbreviation MT is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another.On a basic...
(MT) evaluation, need a model for this variability phenomenon in order to recognize that a particular target meaning can be inferred from different text variants. In 2004 Recognizing Textual Entailment (RTE) has been proposed as a generic task that captures major semantic inference needs across many natural language processing applications.
Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarity of the texts involved.