BibSonomy publications for /user/diego_ma/inf_extractionFri Dec 14 02:49:40 CET 2007Hillsdale (New Jersey), Hove and LondonText-based Intelligent Systems: current research and practice in information extraction and retrieval1992inf_extraction inf_retrieval Fri Dec 14 02:46:54 CET 2007Proceedings TREC 8 (1999)Information Extraction Supported Question Answering2000question_answering inf_extraction This paper discusses the use of our information extraction (IE) system, Textract, in the question answering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (i) IE can provide solid support for QA; (ii) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (iii) a robust natural language shallow parser provides a structural basis for handling questions; (iv) high-level domain independent IE, i.e. extraction of multiple relationships and general events, is expected to bring about a breakthrough in QA.Fri Dec 14 02:44:16 CET 2007San Diego, CAProc. {MUC}-7\myurl{http://www.muc.saic.com}Proc. of the Seventh Message Understanding Conference ({MUC}-7)1998inf_extraction Fri Dec 14 02:44:15 CET 2007Los Altos, CAProceedings of the 6th {ARPA} Message Understanding ConferenceUniversity of {Durham}: description of the {LOLITA} system as used in {MUC}-61996inf_extraction Fri Dec 14 02:43:22 CET 2007Markup Languages389-113{XML} Tools and Architecture for Named Entity Recognition11999inf_extraction XML This paper reports on the development of a Named Entity recognition system developed fully within the xml paradigm.Fri Dec 14 02:42:29 CET 2007Los Altos, CAProceedings of the 6th {ARPA} Message Understanding ConferenceUniversity of {Manitoba}: description of the {PIE} system used for {MUC}-61996inf_extraction Fri Dec 14 02:41:52 CET 2007Satisfying constraints on extraction and adjunction1998inf_extraction Fri Dec 14 02:41:50 CET 2007Proc. IJCAI-97Wrapper Induction for Information Extraction1997inf_extraction Wrapper Induction for Information Extraction by Nicholas Kushmerick Chairperson of Supervisory Committee: Professor Daniel S. Weld Department of Computer Science and Engineering The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Wrappers are often used for this purpose. A wrapper is a procedure for extracting a particular ...Fri Dec 14 02:40:56 CET 2007Proc. MUC-7On-line proceedings, \myurl{http:\\www.muc.saic.com/}University of {S}heffield: Description of the {LaSIE-II} System and Used for {MUC-7}1998inf_extraction GATE The University of Sheffield NLP group took part in MUC-7 using the LaSIE-II system, an evolution of the LaSIE (Large Scale Information Extraction) system first created for participation in MUC-6 and part of a larger research effort into information extraction underway in our group. LaSIE-II was used to carry out all five of the MUC-7 tasks and was, in fact, the only system to take part in all of the MUC-7 tasks. While LaSIE-II is significantly different from the earlier version (differences are detailed below) there are no radical changes in the basic philosophy of the approach. This could be described as seeking a pragmatic middle way in the shallow vs deep analysis debate which has characterised the last several MUCs. That is, while aware that information extraction tasks may not require full text understanding, and hence that systems should be optimised to make use of shallow techniques where appropriate, we have not wanted to preclude the application of arbitrarily sophisticated linguistic analysis techniques where these may prove useful. The result is an eclectic mixture of techniques including finite state recognition of domain-specific lexical patterns, partial parsing using a restricted context-free grammar, simplified semantic representation of each sentence in the text and a formal representation of the whole discourse from which all of the IE task results and the coreference task results are derived. From our perspective, LaSIE-II should not be viewed as the expression of a theory about how to do IE, but as a laboratory in which ongoing experiments with different component NL processing techniques, and most importantly, their interaction are being carried out. Seen this way, one of the most important developments in LaSIE-II is its modularised architecture and integration into the GATE platform (see below) which has enabled us to gain much deeper insights into strengths and weaknesses of components of the system and the ways in which these interact.Fri Dec 14 02:40:53 CET 2007Proceedings of the Pacific Symposium on Biocomputing' 00 (PSB'00)502-513Two Applications of Information Extraction to Biological Science Journal Articles: Enzyme Interactions and Protein Structures2000inf_extraction named_entities Information extraction technology, as defined and developed through the U.S. DARPA Message Understanding Conferences (MUCs), has proved successful at extracting information primarily from newswire texts and primarily in domains concerned with human activity. In this paper we consider the application of this technology to the extraction of information from scientific journal papers in the area of molecular biology. In particular, we describe how an information extraction system designed to participate in the MUC exercises has been modified for two bioinformatics applications: EMPathIE, concerned with enzyme and metabolic pathways; and PASTA, concerned with protein structure. Progress to date provides convincing grounds for believing that IE techniques will deliver novel and effective ways for scientists to make use of the core literature which defines their disciplines.Fri Dec 14 02:40:39 CET 2007Cambridge, MAFinite State Devices for Natural Language Processing{FASTUS}: A Cascaded Finite-state Transducer for Extracting Information from Natural-language Text1996inf_extraction Fri Dec 14 02:40:28 CET 2007Menlo Park, CAThe {TACITUS} system: The {MUC}-3 experience1991inf_extraction Fri Dec 14 02:39:57 CET 2007Proc. LREC-2000Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction2000inf_extraction WordNet In this paper we present a new method of automatic acquisition of linguistic patterns for Information Extraction, as implemented in the CICERO system. Our approach combines lexico-semantic information available from the WordNet database with collocating data extracted from training corpora. Due to the open-domain nature of the WordNet information and the immediate availability of large collections of texts, our method can be easily ported to open-domain Information Extraction.Fri Dec 14 02:38:38 CET 2007Gold CoastProceedings of IPMM'97JulyCan Syntactic Processing Help Extracting Information from Texts?1997inf_extraction Fri Dec 14 02:37:49 CET 2007Communications of the {ACM}January180-91Information Extraction391996inf_extraction Fri Dec 14 02:36:42 CET 2007IBM Systems JournalDec4985-1001Towards Speech as a Knowledge Resource402001speech inf_retrieval inf_extraction Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is very hard, and extracting knowledge from speech is even harder. Nevertheless, the potential reward for solving this problem drives us to pursue it. Before we can exploit speech as a knowledge resource, however, we must understand the current state of the art in speech recognition and the relevant, successful applications of speech recognition in the related areas of multimedia indexing and search. In this paper we advocate the study of speech as a knowledge resource, provide a brief introduction to the state of the art in speech recognition, describe a number of systems that use speech recognition to enable multimedia analysis, indexing, and search, and present a number of exploratory applications of speech recognition that move toward the goal of exploiting speech as a knowledge resource...Fri Dec 14 02:35:54 CET 2007Proceedings of the Sixth {Text} {REtrieval} {Conference}Using Information Extraction to Improve Document Retrieval1997inf_retrieval inf_extraction Fri Dec 14 02:35:42 CET 2007Trends in Analytical Chemistry63Challenges for chemical information extraction and text retrieval161997inf_extraction Fri Dec 14 02:35:35 CET 2007Proc. DARPA Broadcast News Workshop51-54Named Entity Recognition in Speech: Approach and Results Using the {TextPro} System1999speech inf_extraction This paper describes the application of the TextPro system to the task of recognition of named entities in speech. TextPro is a lightweight engine for interpreting cascaded finite-state transducers. Although originally intended for processing text, the experience of this evaluation demonstrates the system can easily be adapted to processing transcripts generated by a speech recognizer as well.Fri Dec 14 02:35:34 CET 2007Los Altos, CAProc. {MUC-5}221-235{SRI:} description of the {JV}-{FASTUS} system used for {MUC-5}1993inf_extraction