natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...

, semantic role labeling (also called

shallow semantic parsing In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of a ...

or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their

semantic role In certain theories of linguistics, thematic relations, also known as semantic roles, are the various roles that a noun phrase may play with respect to the action or state described by a governing verb, commonly the sentence's main verb. For ex ...

in the sentence, such as that of an

agent Agent may refer to: Espionage, investigation, and law *, spies or intelligence officers * Law of agency, laws involving a person authorized to act on behalf of another ** Agent of record, a person with a contractual agreement with an insuran ...

, goal, or result. It serves to find the meaning of the sentence. To do this, it detects the arguments associated with the predicate or

verb A verb () is a word ( part of speech) that in syntax generally conveys an action (''bring'', ''read'', ''walk'', ''run'', ''learn''), an occurrence (''happen'', ''become''), or a state of being (''be'', ''exist'', ''stand''). In the usual descr ...

of a sentence and how they are classified into their specific roles. A common example is the sentence "Mary sold the book to John." The agent is "Mary," the predicate is "sold" (or rather, "to sell,") the theme is "the book," and the recipient is "John." Another example is how "the book belongs to me" would need two labels such as "possessed" and "possessor" and "the book was sold to John" would need two other labels such as theme and recipient, despite these two clauses being similar to "subject" and "object" functions.

History

In 1968, the first idea for semantic role labeling was proposed by Charles J. Fillmore. His proposal led to the FrameNet project which produced the first major computational lexicon that systematically described many predicates and their corresponding roles. Daniel Gildea (Currently at

University of Rochester The University of Rochester (U of R, UR, or U of Rochester) is a private university, private research university in Rochester, New York. The university grants Undergraduate education, undergraduate and graduate degrees, including Doctorate, do ...

, previously

University of California, Berkeley The University of California, Berkeley (UC Berkeley, Berkeley, Cal, or California) is a public land-grant research university in Berkeley, California. Established in 1868 as the University of California, it is the state's first land-grant u ...

/ International Computer Science Institute) and Daniel Jurafsky (currently teaching at Stanford University, but previously working at

University of Colorado The University of Colorado (CU) is a system of public universities in Colorado. It consists of four institutions: University of Colorado Boulder, University of Colorado Colorado Springs, University of Colorado Denver, and the University o ...

and

UC Berkeley The University of California, Berkeley (UC Berkeley, Berkeley, Cal, or California) is a public university, public land-grant university, land-grant research university in Berkeley, California. Established in 1868 as the University of Californi ...

) developed the first automatic semantic role labeling system based on FrameNet. The PropBank corpus added manually created semantic role annotations to the Penn Treebank corpus of

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texts. Many automatic semantic role labeling systems have used PropBank as a training dataset to learn how to annotate new sentences automatically.

Uses

Semantic role labeling is mostly used for machines to understand the roles of words within sentences. This benefits applications similar to

Natural Language Processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...

programs that need to understand not just the words of languages, but how they can be used in varying sentences. A better understanding of semantic role labeling could lead to advancements in question answering, information extraction, automatic text summarization, text data mining, and

speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...

References

External links

CoNLL-2005 Shared Task: Semantic Role Labeling

Illinois Semantic Role Labeler
state of the art semantic role labeling syste
Demo

Preposition SRL
Identifies semantic relations expressed by prepositions
Shalmaneser
is another state of the art system for assigning semantic predicates and roles. {{Natural language processing Grammar Computational linguistics Tasks of natural language processing

History

Uses

See also

References

External links