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Usage of word sense disambiguation in concept identification in ontology construction

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Usage of word sense disambiguation in concept identification in ontology construction

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Usage of word sense disambiguation in concept identification in ontology construction

  1. 1. Usage of Word Sense Disambiguation in Concept Identification in Ontology Construction 1 Guest Talk at University of Moratuwa, Department of Computer Science and Engineering 5th November, 2016 Discussed by: Kiruparan Balachandran
  2. 2. Background Information - Ontology Ontology provides a potential method to describe domain knowledge 2 algorithm sorting algorithm problem solve complexity has is a
  3. 3. Background Information - Ontology learning layer-cake approach Terms Relations Concept Hierarchy Concepts Synonyms {Randomized algorithm, sorting algorithm, system software, application software} {Randomized algorithm, sorting algorithm}, {system software, application software} Algorithm (I, E, L) isA(sorting algorithm, algorithm) - known as Taxonomy relationship solve (algorithm, problem) - known as Non- Taxonomy relationship RulesisA(sorting algorithm, algorithm) -> solve (sorting algorithm, problem) 3
  4. 4. Implemented approach follows Buitelaar et al. criteria in forming concepts from terms • An intentional definition of the concept • Formal definition: A term can be considered as a concept if the term is linked with a valid relation to another term. • Informal definition: A term should have a textual description. • A set of concept instances, i.e. its extensions: a term can be considered a concept if it has instances. • A set of linguistic realizations. 4
  5. 5. Feed (ts and to separately) referred as t and sentence ts Subject Phrase and Object Phrase identified in each sentence Iterate each sentence (ts) from the corpus Identify sense tsense related to domain from the list of sense (disambiguating sense) List of sense exist in WordNet for t Full or part of subject phrases (ts) and object phrases (to) exist in the list of domain-specific 5 Need of WSD in forming concepts from terms If tsense is exist for both tsense of ts and to are candidate for domain-specific concepts For example ts = “we propose a hardware design, call the virtual line scheme, that allows the utilization of large virtual cache line when fetch datum from memory for better exploitation of spatial locality”
  6. 6. cache#n#1, cache#n#2, and cache#n#3 Feed (ts and to separately) referred as t and sentence ts Subject Phrase and Object Phrase identified in each sentence Iterate each sentence (ts) from the corpus Identify sense tsense related to domain from the list of sense (disambiguating sense) List of sense exist in WordNet for t Full or part of subject phrases (ts) and object phrases (to) exist in the list of domain-specific 6 Need of WSD in forming concepts from terms If tsense is exist for both tsense of ts and to are candidate for domain-specific concepts
  7. 7. Which algorithm best suited ? • LESK • Original LESK • definition of a word meaning as a only source of contextual information for a given sense • combinatorial explosion • Use of Simulated annealing 7
  8. 8. Which algorithm best suited ? • LESK • Original LESK • definition of a word meaning as a only source of contextual information for a given sense • combinatorial explosion • Use of Simulated annealing • Simplified LESK • To solve combinatorial explosion • Runs a separate disambiguation process for each ambiguous word in the input text • Adapted LESK • Enlarged context : consider hypernyms, hyponyms, holonyms, meronyms, troponyms, attribute relations, and their associated definitions 8 Less accuracy
  9. 9. Which algorithm best suited ? • Other well known algorithms with good performance use • Path • Depth of least common ancestor (LCS) referred as WUP • Path length and path direction referred as HSO • Link strength of a parent-child link using corpus statistical information 9 ConSim (C1, C2) = 2∗N3 N1+N2+2∗N3 root C3 C1 C2 N1 N2 N3
  10. 10. Which algorithm best suited ? • Other well known algorithms with good performance use • Path • Depth of least common ancestor (LCS) referred as WUP • Path length and path direction referred as HSO • Link strength of a parent-child link using corpus statistical information 10 Weight = C – path length – k * number of changes of direction
  11. 11. Which algorithm best suited ? • Link strength of a parent-child link using corpus statistical information 11 Information content + distance Information Content : obtained by estimating probability of occurrence of class in a large text corpus
  12. 12. For each sense Extract the informal definition of sense from WordNet Calculating the similarity between ts and WNsn by calculating similarity matrix between ts and WNsn using a LESK algorithm. The value is normalized based on number of entries in the distance matrix. Return the synset, which has high similarity value 12 Disambiguating Concepts (LESK ?) cache#n#1, cache#n#2, and cache#n#3
  13. 13. For each sense Extract the informal definition of sense from WordNet Calculating the similarity between ts and WNsn by calculating similarity matrix between ts and WNsn using a LESK algorithm. The value is normalized based on number of entries in the distance matrix. Return the synset, which has high similarity value 13 Disambiguating Concepts (LESK ?) For example • WNs1 e.g. “a hidden storage space for money or provisions or weapons” • WNs2 e.g. “a secret store of valuables or money” • WNs3 e.g. “RAM memory that is set aside as a specialized buffer storage, which is continually updated; used to optimize data transfers between system elements with different characteristics”
  14. 14. For each sense Extract the informal definition of sense from WordNet Calculating the similarity between ts and WNsn by calculating similarity matrix between ts and WNsn using a LESK algorithm. The value is normalized based on number of entries in the distance matrix. Return the synset, which has high similarity value 14 Disambiguating Concepts (LESK ?)
  15. 15. For each sense Extract the informal definition of sense from WordNet Calculating the similarity between ts and WNsn by calculating similarity matrix between ts and WNsn using a LESK algorithm. The value is normalized based on number of entries in the distance matrix. Return the synset, which has high similarity value 15 Disambiguating Concepts (LESK ?)
  16. 16. Evaluation – domain-specific concept extraction Annotator 1 Annotator 2 Annotator 3 ComSciPrecision for concepts 75% 56% 78% Our approach MaxMatcher discussed by Zhou et al. BioAnnotator Subramaniam et al. Bio MedicalRecall 58.70% 57.73% 20.27% • Identified 253 computer science domain-specific concepts validated by three domain experts • Measured the inter-annotator agreement using Fleiss' kappa • 0.36712, a fair agreement (3 annotators, 253concepts, 2 categories) • Identified 47 domain-specific concepts for the GENIA corpus • compared with two different approaches discussed by Zhou et al. and Subramaniam et al. 16
  17. 17. Why LESK ? 17 Conclusion Choosing a best WSD algorithm based on • Nature of your problem • Available factors • Performance with respect to accuracy and time
  18. 18. References 18 K. Balachandran and S. Ranathunga, "Domain-Specific Term Extraction for Concept Identification in Ontology Construction", in IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 2016, pp. 34-41. P. Buitelaar, P. Cimiano, and B. Magnini, Ontology learning from text: methods, evaluation and applications vol. 123: IOS press, 2005. X. Zhou, X. Zhang, and X. Hu, "MaxMatcher: Biological concept extraction using approximate dictionary lookup," in PRICAI 2006: Trends in Artificial Intelligence, ed: Springer, 2006, pp. 1145-1149. L. V. Subramaniam, S. Mukherjea, P. Kankar, B. Srivastava, V. S. Batra, P. V. Kamesam, et al., "Information extraction from biomedical literature: methodology, evaluation and an application," in Proceedings of the twelfth international conference on Information and knowledge management, 2003, pp. 410-417. G. Hirst and D. St-Onge, "Lexical chains as representations of context for the detection and correction of malapropisms," WordNet: An electronic lexical database, vol. 305, pp. 305-332, 1998. S. Banerjee and T. Pedersen, "An adapted Lesk algorithm for word sense disambiguation using WordNet," in Computational linguistics and intelligent text processing, ed: Springer, 2002, pp. 136-145. Z. Wu and M. Palmer, "Verbs semantics and lexical selection," in Proceedings of the 32nd annual meeting on Association for Computational Linguistics, 1994, pp. 133-138. M. Lesk, "Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone," in Proceedings of the 5th annual international conference on Systems documentation, 1986, pp. 24-26. C. Leacock and M. Chodorow, “Combining Local Context and Wordnet Similarity for Word Sense Disambiguation,” WordNet: An Electronic Lexical Database, vol. 49, pp. 265- 283, MIT Press, 1998. J. J. Jiang and D. W. Conrath, “Semantic similarity based on corpus statistics and lexical taxonomy,” in Proc. Int. Conf. Research in Computational Linguistics, 1998, pp. 19–33.
  19. 19. Questions ? Thank You… 19

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