BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction

Kamal Taha; Mohammed Al Zaabi

doi:10.1109/CIDM.2014.7008151

BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction

Kamal Taha, Mohammed Al Zaabi

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called BioHCDP. BioHCDP aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined above, as follows: (1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules, and (2) it applies semantic relationship extraction models that represent the relationships of different patterns of usage in different contexts. BioHCDP can be used to extract various classes of data from biological texts, including protein function assignments, genetic networks, and protein-protein interactions. We compared BioHCDP experimentally with three systems. Results showed marked improvement.

Original language	British English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014
Subtitle of host publication	2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	78-85
Number of pages	8
ISBN (Electronic)	9781479945191
DOIs	https://doi.org/10.1109/CIDM.2014.7008151
State	Published - 13 Jan 2015
Event	5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Publication series

Name	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings

Conference

Conference	5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

Keywords

biological NLP
biomedical literature
dependency parsers
information extraction
Text mining

Access to Document

10.1109/CIDM.2014.7008151

Cite this

Taha, K., & Al Zaabi, M. (2015). BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings (pp. 78-85). Article 7008151 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIDM.2014.7008151

Taha, Kamal ; Al Zaabi, Mohammed. / BioHCDP : A hybrid constituency-dependency parser for biological NLP information extraction. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 78-85 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings).

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title = "BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction",

abstract = "One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called BioHCDP. BioHCDP aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined above, as follows: (1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules, and (2) it applies semantic relationship extraction models that represent the relationships of different patterns of usage in different contexts. BioHCDP can be used to extract various classes of data from biological texts, including protein function assignments, genetic networks, and protein-protein interactions. We compared BioHCDP experimentally with three systems. Results showed marked improvement.",

keywords = "biological NLP, biomedical literature, dependency parsers, information extraction, Text mining",

author = "Kamal Taha and {Al Zaabi}, Mohammed",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014 ; Conference date: 09-12-2014 Through 12-12-2014",

year = "2015",

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doi = "10.1109/CIDM.2014.7008151",

language = "British English",

series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings",

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Taha, K & Al Zaabi, M 2015, BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings., 7008151, IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 78-85, 5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/CIDM.2014.7008151

BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction. / Taha, Kamal; Al Zaabi, Mohammed.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 78-85 7008151 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Al Zaabi, Mohammed

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N2 - One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called BioHCDP. BioHCDP aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined above, as follows: (1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules, and (2) it applies semantic relationship extraction models that represent the relationships of different patterns of usage in different contexts. BioHCDP can be used to extract various classes of data from biological texts, including protein function assignments, genetic networks, and protein-protein interactions. We compared BioHCDP experimentally with three systems. Results showed marked improvement.

AB - One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called BioHCDP. BioHCDP aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined above, as follows: (1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules, and (2) it applies semantic relationship extraction models that represent the relationships of different patterns of usage in different contexts. BioHCDP can be used to extract various classes of data from biological texts, including protein function assignments, genetic networks, and protein-protein interactions. We compared BioHCDP experimentally with three systems. Results showed marked improvement.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Taha K, Al Zaabi M. BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 78-85. 7008151. (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings). doi: 10.1109/CIDM.2014.7008151

BioHCDP: A hybrid constituency-dependency parser for biological NLP information extraction

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