Coreference resolution using expressive logic models

ABSTRACT

Coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. Traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. However, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. In this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the Markov Logic Network. The proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. Moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. Our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.

REFERENCES

• 1: A. Bagga and B. Baldwin. Algorithms for scoring coreference chains. In Proceedings of the Linguistic Coreference Workshop at the First International Conference on Language Resources and Evaluation, pages 563--566, 1998.
• 2: S. Kok and P. Domingos. The alchemy system for statistical relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2005. http://www.cs.washington.edu/ai/alchemy
• 3: Vincent Ng, Machine learning for coreference resolution: from local classification to global ranking, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, p.157-164, June 25-30, 2005, Ann Arbor, Michigan [doi>10.3115/1219840.1219860]
• 4: Matthew Richardson , Pedro Domingos, Markov logic networks, Machine Learning, v.62 n.1-2, p.107-136, February 2006 [doi>10.1007/s10994-006-5833-1]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Design, Experimentation

Keywords:
coreference resolution, natural language processing