Identifying non-referential it: a machine learning approach incorporating linguistically motivated patterns (2005) [4 citations — 0 self]
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by Adriane Boyd
In Proceedings of the ACL Workshop on Feature Selection for Machine Learning in NLP, Ann Arbor
http://acl.ldc.upenn.edu/W/W05/W05-0406.pdf
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Abstract:
In this paper, we present a machine learning system for identifying non-referential it. Types of non-referential it are examined to determine relevant linguistic patterns. The patterns are incorporated as features in a machine learning system which performs a binary classification of it as referential or non-referential in a POS-tagged corpus. The selection of relevant, generalized patterns leads to a significant improvement in performance. 1
Citations
| 200 | An algorithm for pronominal anaphora resolution – Lappinen, Leass - 1994 |
| 188 | A.(2004), TiMBL: Tilburg Memory Based Learner, version 5.1, reference guide – Daelemans, Zavrel, et al. |
| 52 | 2000, ‘User Reference Guide for the British National Corpus – Burnard |
| 39 | The Cambridge Grammar of the English Language – Huddleston, Pullum - 2002 |
| 34 | Applied morphological processing of English – Minnen, Carroll, et al. - 2001 |
| 15 | Towards the Automatic Recognition of Anaphoric Features – Paice, Husk - 1987 |
| 13 | Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution – Ng, Cardie - 2002 |
| 8 | Automatic resolution of anaphora in english – Denber - 1998 |
