Improving disambiguation in FASIT

Burgin, Robert; Dillon, Martin

doi:10.1002/(sici)1097-4571(199203)43:2<101::aid-asi1>3.0.co;2-y

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…First, an earlier study (Craven, 1991a) had shown little use in abstracts of longer verbatim word sequences from full texts. Second, keyword extraction is a somewhat simpler task than phrase extraction, though methods for efficient phrase extraction do exist, as in INDEX (Jones, Gassie, & Podhakkrishnan, 1990), FASIT (Burgin & Dillon, 1992), CLARIT (Paijmans, 1993), and work reported by Fagan (1989).…”

Section: Introductionmentioning

confidence: 99%

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Craven

2000

J. Am. Soc. Inf. Sci.

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Experimental subjects wrote abstracts of articles using a simplified version of the TEXNET abstracting assistance software. In addition to the full text, subjects were presented with either keywords or phrases extracted automatically. The resulting abstracts, and the times taken, were recorded automatically; some additional information was gathered by oral questionnaire. Selected abstracts produced were evaluated on various criteria by independent raters. Results showed considerable variation among subjects, but 37% found the keywords or phrases “quite” or “very” useful in writing their abstracts. Statistical analysis failed to support several hypothesized relations: phrases were not viewed as significantly more helpful than keywords; and abstracting experience did not correlate with originality of wording, approximation of the author abstract, or greater conciseness. Requiring further study are some unanticipated strong correlations including the following: Windows experience and writing an abstract like the author's; experience reading abstracts and thinking one had written a good abstract; gender and abstract length; gender and use of words and phrases from the original text. Results have also suggested possible modifications to the TEXNET software.

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Section: Introductionmentioning

confidence: 99%

Abstracts produced using computer assistance

Craven

2000

J. Am. Soc. Inf. Sci.

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“…An original assumption of the author, that ltering performance could be used as a tness function, measuring the quality of the parsing produced by a grammar, has proved to have little supported. As with the earlier work of Burgin and Dillon (1992), relatively little improvement was noted when additional linguistic knowledge becomes available about document components, although any ltering or retrieval improvement is welcome, no matter how small! While using retrieval performance as a tness function does work, we believe that other functions will allow for more rapid learning of the characteristics of natural language.…”

Section: Discussionmentioning

confidence: 76%

Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering: An empirical basis for grammatical rules

Losee

1996

Information Processing & Management

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The grammars of natural languages may be learned by using genetic algorithms that reproduce and mutate grammatical rules and part-of-speech tags, improving the quality of later generations of grammatical components. Syntactic rules are randomly generated and then evolve; those rules resulting in improved parsing and occasionally improved retrieval and filtering performance are allowed to further propagate. The LUST system learns the characteristics of the language or sublanguage used in document abstracts by learning from the document rankings obtained from the parsed abstracts. Unlike the application of traditional linguistic rules to retrieval and filtering applications, LUST develops grammatical structures and tags without the prior imposition of some common grammatical assumptions (e.g., part-of-speech assumptions), producing grammars that are empirically based and are optimized for this particular application. ¡The author wishes to thank Stephanie Haas for discussions during the course of this research.

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“…While using natural language processing techniques such as part-of-speech tagging may improve retrieval and filtering performance, the degree of improvement with word-sense disambiguation may vary from small to moderate amounts (Burgin & Dillon, 1992;Ide & Veronis, 1998;Krovetz & Croft, 1992;Sanderson, 1994;Strzalowski, 1995). Wilks and Stevenson (1998) found in a small test that "92% of content word tokens can be disambiguated .... using the part-of-speech information produced by a a part-of-speech tagger."…”

Section: Evolution and The Study Of Natural Languagementioning

confidence: 99%

Natural language processing in support of decision-making: phrases and part-of-speech tagging

Losee

2001

Information Processing & Management

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The use of natural language information can improve decision-making. Darwinian considerations suggest that language may have developed because it leads to improved decision making and survival, justifying the study of language's contribution to decision making. The study of information-based decision making within the context of evolution provides a view of information use that allows us to both describe the phenomenon of information use as well as to explain why an information use occurs as it does. Increasing information retrieval performance using phrases and part-of-speech (POS) information is one example of a type of decision-making performance that is improved when using this linguistic information. By studying a set of phrases used in a text retrieval system, we are able to show the relative effectiveness of using multi-term phrases as opposed to individual terms, as well as the relative worth of POS tagged terms or phrases, as opposed to untagged terms or phrases. An explanation is suggested for why POS tags contribute less to higher order grammatical constructs. We propose a measure of those needs for POS disambiguation that can be addressed by tagging; some example terms are analyzed using this measure, and specific degrees of ambiguity are proposed.

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Improving disambiguation in FASIT

Cited by 15 publications

References 10 publications

Abstracts produced using computer assistance

Abstracts produced using computer assistance

Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering: An empirical basis for grammatical rules

Natural language processing in support of decision-making: phrases and part-of-speech tagging

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