JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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MiniPar Tianyong Hao and Yingying Qu With the previous processing steps, the framework can discover concepts with their relations, which are then transformed to be formal knowledge representations. Furthermore, this framework can be extended to more corpora or web documents to extract a large amount of knowledge automatically. SENTENCE LABELING AND FREQUENT PATTERN MINING The pre-processed sentences from domain corpus are used for sentence structure labelling and frequent structural pattern mining. A pattern is defined as the core structure of a sentence, which can then be extended as the generalization of similar sentences that shares the similar structure. Sentence Labeling Domain corpus Preprocessing Sentence labeling Frequent pattern mining Concepts patterns Semantic bank Concept annotating WordNet Relations extracting Pattern matching Relations Fig.1: A framework of the automatic domain knowledge acquisition Knowledge transforming There are many syntax analysis tools, such as TreeTagger (2012) and OpenNLP (2012), to label sentences by identifying nouns and verbs. However, most of these tools parse sentences by part-of-speech tagging without considering the relations between the objects in the sentences, making it difficult to find the core structures. Dependency Grammar (2012) is a class of syntactic theories developed by Lucien Tesnière. Sentence structure is determined by the relations between a word (a head) and its dependents, which are distinct from phrase structure grammars. The dependency relationship in this model is an asymmetric relationship between a word called head (governor) and another one called modifier. This kind of relationship can be used to analyze the dependency thus to acquire the main structure and nouns of a sentence effectively. 172 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) Pattern database Knowledge base
Toward Automatic Semantic Annotating and Pattern Mining for Domain Knowledge Acquisition In this study, Minpar was used as a dependency parser tool to analyze sentence relationship in the paper. Minipar is a principle-based broad-coverage parser for the English language (Berwick et al., 1991). Like Principar, Minipar represents its grammar as a network where nodes represent grammatical categories and links represent types of syntactic (dependency) relationships. The output of Minipar is a dependency tree. The links in the diagram represent dependency relationships. The direction of a link is from the head to the modifier in the relationship. Labels associated with the links represent the types of dependency relations. Furthermore, according to the evaluation with the SUSANNE corpus, Minpar achieves about 88% precision and 80% recall, in relation to dependency relationships (2012). The reliable performance ensures the quality of sentence labelling work. By using Minipar, the sentences in corpus can be labelled with part-of-speech and dependency relations. Therefore, the core sentence structure, which includes nouns, verbs and other meaningful terms, could be extracted. For example, the sentence “what are the risks of plugging a hole in the heart?” was analyzed by using Minipar and the result is shown in Table 1 below. TABLE 1: Minipar output on the example sentence E1 (() fin C * ) 1 (what ~ N E1 whn (gov fin)) 2 (are be VBE E1 i (gov fin)) E4 (() what N 4 subj (gov risk) 3 (the ~ Det 4 det (gov risk)) 4 (risks risk N 2 pred (gov be)) 5 (of ~ Prep 4 mod (gov risk)) E0 (() vpsc C 5 pcomp-c (gov of)) E2 (() ~ N E0 s (gov vpsc)) 6 (plugging plug V E0 i (gov vpsc)) E5 (() ~ N 6 subj (gov plug) 7 (a ~ Det 8 det (gov hole)) 8 (hole ~ N 6 obj (gov plug)) 9 (in ~ Prep 8 mod (gov hole)) 10 (the ~ Det 11 det (gov heart)) 11 (heart ~ N 9 pcomp-n (gov in)) 12 (? ~ U * punc) In order to facilitate high speed pattern extraction, the output of Minipar is firstly converted into XML format. The nodes in XML are aligned with hierarchical structure, which can dramatically speed up pattern extraction. The XML format of the example is shown in Fig.2. After the analysis, a group of nouns such as “risk”, “hole” and “heart”, as well as noun phrases, could be obtained. With the tagged relations, “risk” has the relation of “gov” to “be”, “hole” has the relation of “gov” to “plug”, and “heart” has the relation of “gov” to “in”. Since “gov” means the actual word that it refers to, the structural patterns can be acquired level by Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 173
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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ut bovine milk allergy by far is th
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Application of Anthropometric Dimen
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Different Media Formulation on Bioc
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Y. Yusuf, J. M. Juoi, Z. M. Rosli,
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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Assessment of Heavy Metal Pollution
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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S. Ismail and K. A. Mohd Atan 4 4 p
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Che Mustapha Yusuf, J., Mohd Su’u
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REFERENCES Yogan Jaya Kumar, Naomie
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Hasiah Mohamed@Omar, Azizah Jaafar
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The Role of Similarity Measurement
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TABLE 5: Winners MRS The Role of Si
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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Usability of Educational Computer G
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