2001 ScenearioCustomizationForIE

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Subject Headings: Information Extraction Algorithm, Proteus,

Notes

Cited By

~32 http://scholar.google.com/scholar?cites=5713356703471600379

Quotes

Abstract

  • Information Extraction (IE) is an emerging NLP technology, whose function is to process unstructured, natural language text, to locate specific pieces of information, or facts, in the text, and to use these facts to fill a database.
  • Construction of a pattern base for a new topic is recognized as a time-consuming and expensive process — a principal roadblock to wider use of IE technology in the large. An effective pattern base must be precise and have wide coverage.
  • Each record in the table can have a link back to the originating document. The exact place in the document where the event is reported may be highlighted for convenient access.
  • In the context of IE, “meaning” is understood in terms of facts, formally described as a fixed set of semantic objects — entities, relationships among entities, and events in which entities participate. The semantic objects belong to a small number of types, all having fixed regular structure, within a fixed and closely circumscribed subject domain. This regularity of structure allows the objects to be stored in a relational database.
  • the topic of MUC-6 (the Sixth Message Understanding Conference); in this scenario the system seeks events in which highlevel corporate executives left their posts or assumed new ones.
  • IE systems today are commonly based on pattern matching and partial syntactic analysis.2 Patterns consist of regular expressions (RE) and their associated mappings from syntactic to logical form. The patterns are stored in a pattern base, one of the knowledge bases (KBs) used by the IE system.
  • Performance: Zipf’s Law, a.k.a. the “long tail” syndrome, is a serious problem in IE, as in other areas of NLP. A large number of facts are covered by a small number of frequently occurring patterns, while the remaining facts — the tail of the distribution — are covered by many more rare patterns.
  • Named Entity (NE): find and categorize (certain classes of) proper names appearing in text.
  • Template Relation (TR): find instances of broader relations among entities, such as the “employment” relation between persons and companies, or the “parent/subsidiary relation” between companies.
  • [51] shows how bootstrapping can effectively train a “concept spotter”, a classifier for proper names or noun phrases, by learning patterns stated in terms of short-range lexical items.
  • [59] uses bootstrapping for word-sense disambiguation; though it’s a somewhat different problem from ours, the ideas have important similarities — which attest to the generality and strength of the overall principles that unify all this research.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2001 ScenearioCustomizationForIERoman YangarberScenario Customization for Information ExtractionDoctoral Dissertationhttp://nlp.cs.nyu.edu/publication/papers/yangarber thesis.ps.gz10.1.1.75.90172001
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