Algorithms that learn to extract information

Abstract

All of BBN's research under the TIPSTER III program has focused on doing extraction by applying statistical models trained on annotated data, rather than by using programs that execute handwritten rules. Within the context of MUC-7, the SIFT system for extraction of template entities (TE) and template relations (TR) used a novel, integrated syntactic/semantic language model to extract sentence level information, and then synthesized information across sentences using in part a trained model for cross-sentence relations. At the named entity (NE) level as well, in both MET-1 and MUC-7, BBN employed a trained, HMM-based model. The results in these TIPSTER evaluations are evidence that such trained systems, even at their current level of development, can perform roughly on a par with those based on rules handtailored by experts. In addition, such trained systems have some significant advantages: • They can be easily ported to new domains by simply annotating fresh data. • The complex interactions that make rulebased systems difficult to develop and maintain can here be learned automatically from the training data. We believe that improved and extended versions of such trained models have the potential for significant further progress toward practical systems for information extraction.

Feature assembly method for extracting relations in Chinese

“…TRE usually extracts relation instances with predefined relation types, where supervised techniques are commonly used, e.g., kernel methods [69,62,12,52], belief network [47], linear programming [48], maximum entropy [24], support vector machines [76], deep neural networks [63], or predefined rules [35]. They can be roughly classified into feature-based methods and kernelbased methods.…”

InfoXtract: A customizable intermediate level information extraction engine

“…Nymble (Bikel et al, 1999) and NetOwl (Krupka and Hausman, 1998) are among the most widely used NE taggers reflecting a statistical and rule-based approach respectively. Several IE systems such as LasIE (Humphreys, Gaizauskas, Azzam, Huyck, Mitchell and Cunningham 1998) and SIFT (Miller et al ., 1998) have achieved capabilities required of intermediate-level IE such as relationship and event detection. LasIE incorporates co-reference leading to template element filling as well as scenario template filling; the latter involves event co-reference as well as entity co-reference.…”

“…The most successful IE task thus far has been Named Entity (NE) tagging. The state-of-the-art exemplified by systems such as NetOwl (Krupka and Hausman 1998), IdentiFinder (Miller, Michael, Fox, Ramshaw, Schwartz and Stone 1998) and InfoXtract (Srihari, Niu and Li 2000) has reached near human performance, with 90 per cent or above F-measure. On the other hand, the deep level MUC Scenario Template (ST) IE task is designed to extract detailed information for predefined event scenarios of interest.…”

“…A variety of IE engines, reflecting various goals in terms of extraction as well as architectures are now available. Among these, the most widely used are the GATE system from the University of Sheffield (Cunningham, Maynard, Bontcheva, Tablan, Ursu, Dimitrov, Dowman, Aswani and Roberts 2003), the IE components from Clearforest (), SIFT from BBN (Miller et al ., 1998), REES from SRA (Aone and Ramos-Santacruz, 2000) and various tools provided by Inxight (). Of these, the GATE system most closely resembles InfoXtract in terms of its goals as well as the architecture and customization tools.…”

Hedge Trimmer

“…In this paper, we present our technique for producing headlines using a parse-and-trim approach based on the BBN Parser. As described in Miller et al (1998), the BBN parser builds augmented parse trees according to a process similar to that described in Collins (1997). The BBN parser has been used successfully for the task of information extraction in the SIFT system (Miller et al, 2000).…”