A Cascaded Classification Approach to Semantic Head Recognition
Lukas Michelbacher, Alok Kothari, Martin L. Forst, Christina Lioma, Hinrich Schütze · Empirical Methods in Natural Language Processing · 2011
Most NLP systems use tokenization as part of preprocessing. Generally, tokenizers are based on simple heuristics and do not recognize multi-word units (MWUs) like hot dog or black hole unless a precompiled list of MWUs is available. In this paper, we propose a new cascaded model for detecting MWUs of arbitrary length for tokenization, focusing on noun phrases in the physics domain. We adopt a classification approach because -- unlike other work on MWUs -- tokenization requires a completely automatic approach. We achieve an accuracy of 68% for recognizing non-compositional MWUs and show that our MWU recognizer improves retrieval performance when used as part of an information retrieval system.