Identifying non-referentialit
| Autor: | Adriane Boyd, Donna Byron, Whitney Gegg-Harrison |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Learning classifier system
Structured support vector machine Computer science business.industry Active learning (machine learning) InformationSystems_DATABASEMANAGEMENT Online machine learning Multi-task learning Machine learning computer.software_genre ComputingMethodologies_ARTIFICIALINTELLIGENCE TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Computational learning theory Binary classification Selection (linguistics) Artificial intelligence Software_PROGRAMMINGLANGUAGES business computer |
| Zdroj: | Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing - FeatureEng '05. |
| DOI: | 10.3115/1610230.1610238 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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