Identifying Semantic Relations in Text for Information Retrieval and Information Extraction

Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user’s query. It is an integral part of information extraction—extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.

[1]  Catherine Berrut.  Indexing medical reports: The rime approach , 1990, Inf. Process. Manag..

[2]  Lisa F. Rau,et al.  Knowledge organization and access in a conceptual information system , 1987, Inf. Process. Manag..

[3]  Geoffrey Z. Liu.  Semantic vector space model : Implementation and evaluation , 1997 .

[4]  W. Bruce Croft,et al.  The use of phrases and structured queries in information retrieval , 1991, SIGIR '91.

[5]  Ellen Riloff,et al.  An Empirical Study of Automated Dictionary Construction for Information Extraction in Three Domains , 1996, Artif. Intell..

[6]  Clement T. Yu,et al.  A theory of term importance in automatic text analysis , 1974, J. Am. Soc. Inf. Sci..

[7]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[8]  Ellen Riloff,et al.  Automatically Constructing a Dictionary for Information Extraction Tasks , 1993, AAAI.

[9]  Joel L. Fagan.  The effectiveness of a nonsyntatic approach to automatic phrase indexing for document retrieval , 1989 .

[10]  Naomi Sager,et al.  Natural Language Information Processing: A Computer Grammar of English and Its Applications , 1980 .

[11]  Francis Levy.  On the relative nature of relational factors in classifications , 1967, Inf. Storage Retr..

[12]  David Fisher,et al.  Issues in inductive learning of domain-specific text extraction rules , 1995, Learning for Natural Language Processing.

[13]  Syin Chan,et al.  Extracting Causal Knowledge from a Medical Database Using Graphical Patterns , 2000, ACL.

[14]  C. J. van Rijsbergen,et al.  Information Retrieval , 1979, Encyclopedia of GIS.

[15]  Christopher S. G. Khoo,et al.  Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing , 1998 .

[16]  J.E.L. Farradane,et al.  A SCIENTIFIC THEORY OF CLASSIFICATION AND INDEXING AND ITS PRACTICAL APPLICATIONS , 1950 .

[17]  Charles J. Fillmore,et al.  THE CASE FOR CASE. , 1967 .

[18]  Wendy G. Lehnert,et al.  Information extraction , 1996, CACM.

[19]  John F. Sowa,et al.  Conceptual Structures: Information Processing in Mind and Machine , 1983 .

[20]  Donna K. Harman,et al.  The Text REtrieval Conference (TREC) , 1999, NTCIR.

[21]  Sung-Hyon Myaeng,et al.  TIPSTER Panel - DR-LINK's Linguistic-Conceptual Approach to Document Detection , 1992, TREC.

[22]  Christopher S. G. Khoo,et al.  Linguistic Processing of Text for Large-Scale Conceptual Information Retrieval System , 1994, ICCS.

[23]  Shiyali Ramamrita Ranganathan,et al.  The colon classification , 1965 .

[24]  Syin Chan,et al.  A method for extracting causal knowledge from textual databases , 1999 .

[25]  Xin Lu.  On application of case relations to document retrieval , 1992 .

[26]  Emmon W. Bach,et al.  Universals in Linguistic Theory , 1970 .

[27]  Yorick Wilks,et al.  Information Extraction: Beyond Document Retrieval , 1998, Int. J. Comput. Linguistics Chin. Lang. Process..

[28]  Harold L. Somers,et al.  Valency and case in computational linguistics , 1987 .

[29]  Donna Harman,et al.  The First Text REtrieval Conference (TREC-1) , 1993 .

[30]  Jason Farradane,et al.  A SCIENTIFIC THEORY OF CLASSIFICATION AND INDEXING: FURTHER CONSIDERATIONS , 1952 .

[31]  David Fisher,et al.  MITA: An Information-Extraction Approach to the Analysis of Free-Form Text in Life Insurance Applications , 1998, AI Mag..

[32]  David Fisher,et al.  Machine Learning of Text Analysis Rules for Clinical Records , 1999 .

[33]  Martin Dillon,et al.  FASIT: A fully automatic syntactically based indexing system , 1983, J. Am. Soc. Inf. Sci..

[34]  W. Bruce Croft.  Boolean Queries and Term Dependencies in Probabilistic Retrieval Models. , 1986 .

[35]  N Sager,et al.  Natural language processing of asthma discharge summaries for the monitoring of patient care. , 1993, Proceedings. Symposium on Computer Applications in Medical Care.

[36]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[37]  Fujio Nishida,et al.  Structured-information extraction from patent-claim sentences , 1982, Inf. Process. Manag..

[38]  Dan I. Moldovan,et al.  Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction , 1995, IEEE Trans. Knowl. Data Eng..

[39]  Robert N. Oddy,et al.  Using cause-effect relations in text to improve information retrieval precision , 2001, Inf. Process. Manag..

[40]  Maria Teresa Pazienza,et al.  CoDHIR: an information retrieval system based on semantic document representation , 1994, J. Inf. Sci..

[41]  Jason Farradane,et al.  Concept organization for information retrieval , 1967, Information Storage and Retrieval.

[42]  Joel L. Fagan,et al.  The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrieval , 1989, JASIS.

[43]  Alan F. Smeaton,et al.  Experiments on incorporating syntactic processing of user queries into a document retrieval strategy , 1988, SIGIR '88.

[44]  Derek Austin,et al.  PRECIS: A manual of concept analysis and subject indexing , 1974 .

[45]  Lisa F. Rau,et al.  Information extraction and text summarization using linguistic knowledge acquisition , 1989, Inf. Process. Manag..

[46]  Claire Cardie,et al.  Empirical Methods in Information Extraction , 1997, AI Mag..

[47]  Ellen Riloff,et al.  Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing , 1996, Lecture Notes in Computer Science.