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      SORTAL ANAPHORA RESOLUTION IN MEDLINE ABSTRACTS

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      Computational Intelligence
      Wiley

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          A Machine Learning Approach to Coreference Resolution of Noun Phrases

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            Anaphora in Natural Language Understanding: A Survey

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              Is Open Access

              A Maximum Entropy Approach to Identifying Sentence Boundaries

              We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of ., ?, and ! as either a valid or invalid sentence boundary. The training procedure requires no hand-crafted rules, lexica, part-of-speech tags, or domain-specific information. The model can therefore be trained easily on any genre of English, and should be trainable on any other Roman-alphabet language. Performance is comparable to or better than the performance of similar systems, but we emphasize the simplicity of retraining for new domains.
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                Author and article information

                Journal
                Computational Intelligence
                Computational Intell
                Wiley
                0824-7935
                1467-8640
                February 2007
                February 2007
                : 23
                : 1
                : 15-27
                Article
                10.1111/j.1467-8640.2007.00292.x
                9a25ccdb-d132-42dd-a550-b029cd537e44
                © 2007

                http://doi.wiley.com/10.1002/tdm_license_1.1

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