Probabilistic Reasoning for Entity & Relation Recognition

By Dan Roth, Wen-tau Yih. 2002

165 citations recorded by the source corpus.

This paper develops a method for recognizing relations and entities in sentences, while taking mutual dependencies among them into account. e.g., the kill (Johns, Oswald) relation in: "J. V. Oswald was murdered at JFK after his assassin, K. F. Johns. . ." depends on identifying Oswald and Johns as people, JFK being identified as a location, and the kill relation between Oswald and Johns; this, in turn, enforces that Oswald and Johns are people.In our framework, classifiers that identify entities and relations among them are first learned from local information in the sentence; this information, along with constraints induced among entity types and relations, is used to perform global inference that accounts for the mutual dependencies among the entities.Our preliminary experimental results are promising and show that our global inference approach improves over learning relations and entities separately.

Subfields: Artificial Intelligence, Information Extraction

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Citations in the corpus

Modifying Memories in Transformer Models (2020, background): “eralization with better language understanding, Transformers may also replace or assist traditional knowledge bases (KBs) that are either manually curated or require signiﬁcant amount of supervision (Roth and Yih, 2002; Kambhatla, 2004; Surdeanu and Ji, 2014). Different from conventional KBs that ex”
A Linear Programming Formulation for Global Inference in Natural Language Tasks (2004, methodology): “The learning algorithm used is a variation of the Winnow update rule incorporated in SNoW (Roth, 1998; Roth and Yih, 2002), a multi-class classifier that is specifically tailored for large scale learning tasks.”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, unknown): “methods (Kambhatla, 2004) and graphical models (Roth and Yih, 2002).”
Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature (2006, background): “The approach has some resemblance with what was proposed by Roth and Yih (2002).”
Generalized Inference with Multiple Semantic Role Labeling Systems (2005, methodology): “The learning algorithm used is a variation of the update rule incorporated in SNoW (Roth, 1998; Roth and Yih, 2002), a multi-class classi-ﬁer that is tailored for large scale learning tasks.”