Automatic terminology extraction for thematic corpus based on subterm co-occurrence

Chun-Yu Kit

Automatic terminology extraction for thematic corpus based on subterm co-occurrence

Chun-Yu Kit^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In this paper, we present a multi-word terminology extractor for thematic corpus based upon the co-occurrence of subterms. With regard to the basic properties of terminologies, among which we emphasize the structural dependency relation between subterms, a number of straightforward hypotheses are proposed as strategies for terminology recognition. The key idea to measure the structural dependency within a corpus-based approach is that higher frequency of subterm co-occurrence may indicate higher structural dependecy. The experimental results show that our algorithm can extract multi-word terminologies with nice correspondence to domain-specific concepts and notions.

Original language	English
Title of host publication	Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	127-134
Publication status	Published - 1994
Externally published	Yes

Publication series

Name	Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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@inproceedings{dc87ebb8408442b9b3b05ebba8d78928,

title = "Automatic terminology extraction for thematic corpus based on subterm co-occurrence",

abstract = "In this paper, we present a multi-word terminology extractor for thematic corpus based upon the co-occurrence of subterms. With regard to the basic properties of terminologies, among which we emphasize the structural dependency relation between subterms, a number of straightforward hypotheses are proposed as strategies for terminology recognition. The key idea to measure the structural dependency within a corpus-based approach is that higher frequency of subterm co-occurrence may indicate higher structural dependecy. The experimental results show that our algorithm can extract multi-word terminologies with nice correspondence to domain-specific concepts and notions.",

author = "Chun-Yu Kit",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].",

year = "1994",

language = "English",

series = "Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994",

publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",

pages = "127--134",

booktitle = "Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994",

}

Automatic terminology extraction for thematic corpus based on subterm co-occurrence. / Kit, Chun-Yu.
Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 1994. p. 127-134 (Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Automatic terminology extraction for thematic corpus based on subterm co-occurrence

AU - Kit, Chun-Yu

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 1994

Y1 - 1994

N2 - In this paper, we present a multi-word terminology extractor for thematic corpus based upon the co-occurrence of subterms. With regard to the basic properties of terminologies, among which we emphasize the structural dependency relation between subterms, a number of straightforward hypotheses are proposed as strategies for terminology recognition. The key idea to measure the structural dependency within a corpus-based approach is that higher frequency of subterm co-occurrence may indicate higher structural dependecy. The experimental results show that our algorithm can extract multi-word terminologies with nice correspondence to domain-specific concepts and notions.

AB - In this paper, we present a multi-word terminology extractor for thematic corpus based upon the co-occurrence of subterms. With regard to the basic properties of terminologies, among which we emphasize the structural dependency relation between subterms, a number of straightforward hypotheses are proposed as strategies for terminology recognition. The key idea to measure the structural dependency within a corpus-based approach is that higher frequency of subterm co-occurrence may indicate higher structural dependecy. The experimental results show that our algorithm can extract multi-word terminologies with nice correspondence to domain-specific concepts and notions.

UR - https://www.scopus.com/pages/publications/85120309603

UR - https://www.scopus.com/pages/publications/85120309603#tab=impact

M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994

SP - 127

EP - 134

BT - Proceedings of Rocling 7th Computational Linguistics Conference, ROCLING 1994

PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

ER -

Automatic terminology extraction for thematic corpus based on subterm co-occurrence

Abstract

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