Determining whether two terms in text have an ancestor relation (e.g. Toyota and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in NLP applications such as Question Answering, Summarization, and Recognizing Textual Entailment. Significant work has been done on developing stationary knowledge sources that could potentially support these tasks, but these resources often suffer from low coverage, noise, and are inflexible when needed to support terms that are not identical to those placed in them, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a stationary hierarchical structure of terms and relations, we describe a system that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint optimization inference process and use it to leverage an existing knowledge base also to enforce relational constraints among terms and thus improve the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon existing well-known knowledge sources.

@inproceedings{DoRo10,
  author = {Q. Do and D. Roth},
  title = {Constraints based Taxonomic Relation Classification},
  booktitle = {EMNLP},
  pages = {1099-1109},
  month = {10},
  year = {2010},
  address = {Massachusetts, USA},
  url = "http://cogcomp.cs.illinois.edu/papers/DoRo10.pdf",
  funding = {DARPA},
  projects = {Machine Reading},
}