195 research outputs found
New Methods, Current Trends and Software Infrastructure for NLP
The increasing use of `new methods' in NLP, which the NeMLaP conference
series exemplifies, occurs in the context of a wider shift in the nature and
concerns of the discipline. This paper begins with a short review of this
context and significant trends in the field. The review motivates and leads to
a set of requirements for support software of general utility for NLP research
and development workers. A freely-available system designed to meet these
requirements is described (called GATE - a General Architecture for Text
Engineering). Information Extraction (IE), in the sense defined by the Message
Understanding Conferences (ARPA \cite{Arp95}), is an NLP application in which
many of the new methods have found a home (Hobbs \cite{Hob93}; Jacobs ed.
\cite{Jac92}). An IE system based on GATE is also available for research
purposes, and this is described. Lastly we review related work.Comment: 12 pages, LaTeX, uses nemlap.sty (included
Enhanced services for targeted information retrieval by event extraction and data mining
Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament. --
Requirements for Information Extraction for Knowledge Management
Knowledge Management (KM) systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction (IE) technology. However, IE was originally developed for database population and there is a mismatch between what is required to successfully perform KM and what current IE technology provides. In this paper we begin to address this issue by outlining requirements for IE based KM
Enhanced Services for Targeted Information Retrieval by Event Extraction and Data Mining
Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Questionanswering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user’s question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
We describe the CoNLL-2003 shared task: language-independent named entity
recognition. We give background information on the data sets (English and
German) and the evaluation method, present a general overview of the systems
that have taken part in the task and discuss their performance
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