Finding High-frequent Synonyms of a Domain-specific Verb in English Sub-language of

MEDLINE Abstracts Using WordNet

Chun Xiao and Dietmar Rösner

Institut für Wissens-

und Sprachverarbeitung (IWS),

Faculty of Computer Science,University of Magdeburg,

39016 Magdeburg, Germany

Introduction — MEDLINE Abstract

• MEDLINE®: – Domain: clinical medicine, biomedicine, biological and

physical sciences;– Source: articles from over 4,600 journals published

throughout the world;– Coverage: abstracts are included for about 52% of the

articles.

• PubMed®, an application of UMLS (unified medical language system), provides links within MEDLINE® to the full text of 15 clinical medical journals . – Available at: http://www.ncbi.nlm.nih.gov/PubMed/

Available Resources in the Experiment

• The test corpus consists of 800 MEDLINE abstracts extracted from the GENIA Corpus V3.0p and V3.01. - Available at: http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/

• WordNet 1.7.1

Extraction of a Specific Relation

• Inhibitory relation– Example: Secreted from activated T cells and

macrophages, bone marrow-derived MIP-1 alpha/GOS19 inhibits primitive hematopoietic stem cells and appears to be involved in the homeostatic control of stem cell proliferation.

• Semantic annotations in the GENIA corpus: protein_molecule cell_type

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High-frequent Verbs in the Test Corpus

Synonym Sets (Synsets) of Verb inhibit

• Synset in WordNetSense 1suppress, stamp down, inhibit, subdue, conquer, curb => control, hold in, hold, contain, check, curb,

moderateSense 2inhibit => restrict, restrain, trammel, limit, bound, confine,

throttle

• Synset in test corpus of MEDLINE abstractsInhibit, block, prevent, etc.

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Problem

• Occurrences of verbs in the two synsets in the test corpus of MEDLINE abstracts– WN-synonyms: suppress (69), limit (16), restrict (5)

– non WN-synonyms: block (124), reduce (119), prevent(53)

• How can WordNet synsets and information from the corpus be combined to create domain-specific verb synsets?

Three Definitions

• Language unit — a text segment (a sentence, several sentences, or a paragraph, etc.) that expresses one semantic topic.

• Core word — the verb, whose synset in the test corpus is to be found out. E.g., in this test inhibit is the core word.

• Keyword — the word, whose corresponding verb base form is the core word. E.g., in this test inhibitor, inhibiting, and so on are keywords.

Example

We performed an analysis of the mechanisms by which two PKC inhibitors, Calphostin C and Staurosporine, prevent the FN-induced IL-1beta response. Both inhibitors blocked the secretion of IL-1beta protein into the media of peripheral blood mononuclear cells exposed to FN.

• Language unit: two sentences

• Core word: inhibit

• Keyword: inhibitor (2 times)

• Local context: searching window size >=3

• Verbs around the first keyword: perform, prevent, block, expose

• Verbs around the second keyword: prevent, perform, block, expose In the following test, the language unit is selected to be the whole

abstract.

Idea Description• Assumption:

The synonyms of a verb co-occur much more frequently together with the keywords of the verb than together with other words in the language unit.

• Method: Thus the verb chunks around the keywords are

collected, from which the synonyms of the core word will be selected and filtered, using WordNet synset information.

- One resource: WordNet synset information

- The other resource: Local context information in the test corpus

Distribution of Keywords of inhibit in the Test Corpus

Verbs around the Keywords in the Test Corpus

Method Description I • Expansion of WordNet Synsets (Si)

– S1 : the verb collection of synonyms of all synonyms of the core word;

– S2 : the verb collection of synonyms of all verbs in S1;

– …

• Expansion of Stoplist (STOPk)– STOP0: manually select 15 stop-verbs from the high-

frequent verbs in the test corpus (e.g., suggest, indicate, including the high-frequent antonyms of the core word);

– STOP1: the verb collection of synonyms of all verbs in STOP0;

– …

Method Description II

• Verb list from the corpus (Vj) Verbs around the keywords in a local context of

searching window size of j are collected.

• Synonym candidate list (Sg) If a verb is in Vj and also in Si, but not in

STOPk, then add it to Sg.

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Evaluation• Golden standard list (SG)

– A manually created synonym list, which is extracted from the test corpus.

– Consist of 10 verbs with the most frequent occurrences, in which 3 verbs come directly from the WordNet synset of “inhibit”, the rest 7 verbs come from its hypernym set or the expanded list of its synonyms.

• Recall & Precision

Result

60% recall of SG <=> 93.05% occurrences in the test corpus

Conclusions and Future Work

• Conclusions– English sublanguage of MEDLINE abstract;– The core word and its keywords were high-frequent;– Multiword verb structures were not considered yet;– Balance between recall and precision: expansion of Si and

STOPk should be limited.

• Future works– Consideration of other WordNet information besides synsets;– Automatic creation of stoplists;– Extraction of multiword verb structures;– Utilization of syntactic information.

Thanks!

Looking forward to your questions!

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Possible Errors

• Errors of POS tags betweenAdjectives <=> Past participles

• Errors of manual works when selecting stop-verbs

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Question or Hope

Can WordNet provide the possibility for accessing multiword expressions?