An Algorithm for Same-Sentence Pronominal Resolution

AAAI Conferences

Pronoun resolution is a vital part of the more general task of coreference resolution. The aim of coreference resolution is, given a natural language text, to discover the entities mentioned in it and the textual mentions of these entities. A coreference system must correctly identify the sets into which the mentions of the text must be clustered so that each set corresponds to a real-world or imagined entity. As a step in this process, the system has to detect pairs of mentions that are assumed to refer to the same entity with a certain confidence. When one of these mentions is a pronoun, the task is called pronominal resolution. In the following example, the pronoun its refers back to the named entity Dawn Capital (its'antecedent'): Dawn Capital changes its name. Previous work in pronominal resolution ranges from naive syntactic approaches to semantically rich approaches and from supervised to unsupervised learning.


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AI Magazine

Over the last eight years, four workshops on machine learning have been held. Participation in these workshops was by invitation only. In response to the rapid growth in the number of researchers active in machine learning, it was decided that the fifth meeting should be a conference with open attendance and full review for presented papers. Thus, the first open conference on machine learning took place 12 to 14 June 1988 at The University of Michigan at Ann Arbor. Of the 150 papers submitted, 49 were accepted for publication in the conference proceedings (available from Morgan Kaufmann).


The Fifth International Conference on Machine Learning

AI Magazine

Over the last eight years, four workshops on machine learning have been held. Participation in these workshops was by invitation only. In response to the rapid growth in the number of researchers active in machine learning, it was decided that the fifth meeting should be a conference with open attendance and full review for presented papers. Thus, the first open conference on machine learning took place 12 to 14 June 1988 at The University of Michigan at Ann Arbor.



Learning Semantic Grammars with Constructive nduct ive ogic

AAAI Conferences

Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristics in a logic program. Appropriate control rules are learned using a new first-order induction algorithm that automatically invents useful syntactic and semantic categories. Empirical results show that the learned parsers generalize well to novel sentences and outperform previous approaches based on connectionist techniques.