Language Computer Corporation

About: Language Computer Corporation is a based out in . It is known for research contribution in the topics: Question answering & Textual entailment. The organization has 52 authors who have published 99 publications receiving 4149 citations. The organization is also known as: Language Computer (United States).

Using Predicate-Argument Structures for Information Extraction

Abstract: In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.

Automatic Discovery of Part-Whole Relations

Abstract: An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.

Performance issues and error analysis in an open-domain question answering system

Abstract: This paper presents an in-depth analysis of a state-of-the-art Question Answering system. Several scenarios are examined: (1) the performance of each module in a serial baseline system, (2) the impact of feedbacks and the insertion of a logic prover, and (3) the impact of various retrieval strategies and lexical resources. The main conclusion is that the overall performance depends on the depth of natural language processing resources and the tools used for answer finding.

Methods for Using Textual Entailment in Open-Domain Question Answering

Abstract: Work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. In this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (Q/A) systems. In our experiments, we show that when textual entailment information is used to either filter or rank answers returned by a Q/A system, accuracy can be increased by as much as 20% overall.

COGEX: a logic prover for question answering

Abstract: Recent TREC results have demonstrated the need for deeper text understanding methods. This paper introduces the idea of automated reasoning applied to question answering and shows the feasibility of integrating a logic prover into a Question Answering system. The approach is to transform questions and answer passages into logic representations. World knowledge axioms as well as linguistic axioms are supplied to the prover which renders a deep understanding of the relationship between question text and answer text. Moreover, the trace of the proofs provide answer justifications. The results show that the prover boosts the performance of the QA system on TREC questions by 30%.