Improving Name Tagging by Reference Resolution and Relation Detection

Proceedings of the 43rd Annual Meeting of the ACL, pages 411–418, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Improving Name Tagging by Reference Resolution and Relation Detection Heng Ji Department of Computer Science Ralph Grishman New York University New York, NY, 10003, USA hengji@cs.nyu.edu grishman@cs.nyu.edu Abstract Information extraction systems incorpo- rate multiple stages of linguistic analysis. Although errors are typically compounded from stage to stage, it is possible to re- duce the errors in one stage by harnessing the results of the other stages. We dem- onstrate this by using the results of coreference analysis and relation extrac- tion to reduce the errors produced by a Chinese name tagger. We use an N-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing. We obtained thereby a reduction of 24% in spurious and incorrect name tags, and a reduction of 14% in missed tags. 1 Introduction Systems which extract relations or events from a document typically perform a number of types of linguistic analysis in preparation for information extraction. These include name identification and classification, parsing (or partial parsing), semantic classification of noun phrases, and coreference analysis. These tasks are reflected in the evalua- tion tasks introduced for MUC-6 (named entity, coreference, template element) and MUC-7 (tem- plate relation). In most extraction systems, these stages of analysis are arranged sequentially, with each stage using the results of prior stages and generating a single analysis that gets enriched by each stage. This provides a simple modular organization for the extraction system. Unfortunately, each stage also introduces a cer- tain level of error into the analysis. Furthermore, these errors are compounded – for example, errors in name recognition may lead to errors in parsing. The net result is that the final output (relations or events) may be quite inaccurate. This paper considers how interactions between the stages can be exploited to reduce the error rate. For example, the results of coreference analysis or relation identification may be helpful in name clas- sification, and the results of relation or event ex- traction may be helpful in coreference. Such interactions are not easily exploited in a simple sequential model … if name classification is performed at the beginning of the pipeline, it cannot make use of the results of subsequent stages. It may even be difficult to use this information im- plicitly, by using features which are also used in later stages, because the representation used in the initial stages is too limited. To address these limitations, some recent sys- tems have used more parallel designs, in which a single classifier (incorporating a wide range of fea- tures) encompasses what were previously several separate stages (Kambhatla, 2004; Zelenko et al., 2004). This can reduce the compounding of errors of the sequential design. However, it leads to a very large feature space and makes it difficult to select linguistically appropriate features for par- ticular analysis tasks. Furthermore, because these decisions are being made in parallel, it becomes much harder …

Content truncated to 500 words