A Multi-resolution Framework for Information Extraction from Free Text

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 592–599, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics A Multi-resolution Framework for Information Extraction from Free Text Mstislav Maslennikov and Tat-Seng Chua Department of Computer Science National University of Singapore {maslenni,chuats}@comp.nus.edu.sg Abstract Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and depend- ency relations between entities. This paper re-examines the problem at the multi- resolution layers of phrase, clause and sen- tence using dependency and discourse rela- tions. Our multi-resolution framework ARE (Anchor and Relation) uses clausal relations in 2 ways: 1) to filter noisy de- pendency paths; and 2) to increase reliabil- ity of dependency path extraction. The re- sulting system outperforms the previous approaches by 3%, 7%, 4% on MUC4, MUC6 and ACE RDC domains respec- tively. 1 Introduction Information Extraction (IE) is the task of identify- ing information in texts and converting it into a predefined format. The possible types of informa- tion include entities, relations or events. In this paper, we follow the IE tasks as defined by the conferences MUC4, MUC6 and ACE RDC: slot- based extraction, template filling and relation ex- traction, respectively. Previous approaches to IE relied on co- occurrence (Xiao et al., 2004) and dependency (Zhang et al., 2006) relations between entities. These relations enable us to make reliable extrac- tion of correct entities/relations at the level of a single clause. However, Maslennikov et al. (2006) reported that the increase of relation path length will lead to considerable decrease in performance. In most cases, this decrease in performance occurs because entities may belong to different clauses. Since clauses in a sentence are connected by clausal relations (Halliday and Hasan, 1976), it is thus important to perform discourse analysis of a sentence. Discourse analysis may contribute to IE in sev- eral ways. First, Taboada and Mann (2005) re- ported that discourse analysis helps to decompose long sentences into clauses. Therefore, it helps to distinguish relevant clauses from non-relevant ones. Second, Miltsakaki (2003) stated that entities in subordinate clauses are less salient. Third, the knowledge of textual structure helps to interpret the meaning of entities in a text (Grosz and Sidner 1986). As an example, consider the sentences “ABC Co. appointed a new chairman. Addition- ally, the current CEO was retired”. The word ‘ad- ditionally’ connects the event in the second sen- tence to the entity ‘ABC Co.’ in the first sentence. Fourth, Moens and De Busser (2002) reported that discourse segments tend to be in a fixed order for structured texts such as court decisions or news. Hence, analysis of discourse order may reduce the variability of possible relations between entities. To model these factors, we propose a multi- resolution framework ARE that integrates both discourse and dependency relations at 2 levels. ARE aims to filter noisy dependency relations from training and support their evaluation with discourse relations between entities. Additionally, we …

Content truncated to 500 words