32,283 research outputs found
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
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
Learning to Extract Keyphrases from Text
Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)
Concept-based Interactive Query Expansion Support Tool (CIQUEST)
This report describes a three-year project (2000-03) undertaken in the Information Studies
Department at The University of Sheffield and funded by Resource, The Council for
Museums, Archives and Libraries. The overall aim of the research was to provide user
support for query formulation and reformulation in searching large-scale textual resources
including those of the World Wide Web. More specifically the objectives were: to investigate
and evaluate methods for the automatic generation and organisation of concepts derived from
retrieved document sets, based on statistical methods for term weighting; and to conduct
user-based evaluations on the understanding, presentation and retrieval effectiveness of
concept structures in selecting candidate terms for interactive query expansion.
The TREC test collection formed the basis for the seven evaluative experiments conducted in
the course of the project. These formed four distinct phases in the project plan. In the first
phase, a series of experiments was conducted to investigate further techniques for concept
derivation and hierarchical organisation and structure. The second phase was concerned with
user-based validation of the concept structures. Results of phases 1 and 2 informed on the
design of the test system and the user interface was developed in phase 3. The final phase
entailed a user-based summative evaluation of the CiQuest system.
The main findings demonstrate that concept hierarchies can effectively be generated from
sets of retrieved documents and displayed to searchers in a meaningful way. The approach
provides the searcher with an overview of the contents of the retrieved documents, which in
turn facilitates the viewing of documents and selection of the most relevant ones. Concept
hierarchies are a good source of terms for query expansion and can improve precision. The
extraction of descriptive phrases as an alternative source of terms was also effective. With
respect to presentation, cascading menus were easy to browse for selecting terms and for
viewing documents. In conclusion the project dissemination programme and future work are
outlined
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
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