Conditional random fields for term extraction

Xing Zhang; Yan Song; Alex Chengyu Fang

Conditional random fields for term extraction

Xing Zhang
, Yan Song
, Alex Chengyu Fang

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

1 Citation (Scopus)

Abstract

In this paper, we describe how to construct a machine learning framework that utilizes syntactic information in extraction of biomedical terms. Conditional random fields (CRF), is used as the basis of this framework. We make an effort to find the appropriate use for syntactic information, including parent nodes, syntactic paths and term ratios under the machine learning framework. The experiment results show that syntactic paths and term ratios can improve precision of term extraction, including old terms and novel terms. However, the recall rate of novel terms still needs to be increased. This research serves as an example for constructing machine learning based term extraction systems that utilizes linguistic information.

Original language	English
Title of host publication	KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Pages	414-417
Publication status	Published - 2010
Event	International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010 - Valencia, Spain Duration: 25 Oct 2010 → 28 Oct 2010

Conference

Conference	International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010
Place	Spain
City	Valencia
Period	25/10/10 → 28/10/10

Research Keywords

Conditional random fields
Syntactic function
Term extraction
Term ratio

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@inproceedings{751be9b6b8df4b9ba7ae89ed4910a6b3,

title = "Conditional random fields for term extraction",

abstract = "In this paper, we describe how to construct a machine learning framework that utilizes syntactic information in extraction of biomedical terms. Conditional random fields (CRF), is used as the basis of this framework. We make an effort to find the appropriate use for syntactic information, including parent nodes, syntactic paths and term ratios under the machine learning framework. The experiment results show that syntactic paths and term ratios can improve precision of term extraction, including old terms and novel terms. However, the recall rate of novel terms still needs to be increased. This research serves as an example for constructing machine learning based term extraction systems that utilizes linguistic information.",

keywords = "Conditional random fields, Syntactic function, Term extraction, Term ratio",

author = "Xing Zhang and Yan Song and Fang, \{Alex Chengyu\}",

year = "2010",

language = "English",

isbn = "9789898425287",

pages = "414--417",

booktitle = "KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",

note = "International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010 ; Conference date: 25-10-2010 Through 28-10-2010",

}

TY - GEN

T1 - Conditional random fields for term extraction

AU - Zhang, Xing

AU - Song, Yan

AU - Fang, Alex Chengyu

PY - 2010

Y1 - 2010

N2 - In this paper, we describe how to construct a machine learning framework that utilizes syntactic information in extraction of biomedical terms. Conditional random fields (CRF), is used as the basis of this framework. We make an effort to find the appropriate use for syntactic information, including parent nodes, syntactic paths and term ratios under the machine learning framework. The experiment results show that syntactic paths and term ratios can improve precision of term extraction, including old terms and novel terms. However, the recall rate of novel terms still needs to be increased. This research serves as an example for constructing machine learning based term extraction systems that utilizes linguistic information.

AB - In this paper, we describe how to construct a machine learning framework that utilizes syntactic information in extraction of biomedical terms. Conditional random fields (CRF), is used as the basis of this framework. We make an effort to find the appropriate use for syntactic information, including parent nodes, syntactic paths and term ratios under the machine learning framework. The experiment results show that syntactic paths and term ratios can improve precision of term extraction, including old terms and novel terms. However, the recall rate of novel terms still needs to be increased. This research serves as an example for constructing machine learning based term extraction systems that utilizes linguistic information.

KW - Conditional random fields

KW - Syntactic function

KW - Term extraction

KW - Term ratio

UR - https://www.scopus.com/pages/publications/78651424162

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78651424162&origin=recordpage

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

SN - 9789898425287

SP - 414

EP - 417

BT - KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

T2 - International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010

Y2 - 25 October 2010 through 28 October 2010

ER -

Conditional random fields for term extraction

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