Summary of the paper

Title Associative and Semantic Features Extracted From Web-Harvested Corpora
Authors Elias Iosif, Maria Giannoudaki, Eric Fosler-Lussier and Alexandros Potamianos
Abstract We address the problem of automatic classification of associative and semantic relations between words, and particularly those that hold between nouns. Lexical relations such as synonymy, hypernymy/hyponymy, constitute the fundamental types of semantic relations. Associative relations are harder to define, since they include a long list of diverse relations, e.g., """"Cause-Effect"""", """"Instrument-Agency"""". Motivated by findings from the literature of psycholinguistics and corpus linguistics, we propose features that take advantage of general linguistic properties. For evaluation we merged three datasets assembled and validated by cognitive scientists. A proposed priming coefficient that measures the degree of asymmetry in the order of appearance of the words in text achieves the best classification results, followed by context-based similarity metrics. The web-based features achieve classification accuracy that exceeds 85%.
Topics Knowledge Discovery/Representation, Semantics, Text mining
Full paper Associative and Semantic Features Extracted From Web-Harvested Corpora
Bibtex @InProceedings{IOSIF12.536,
  author = {Elias Iosif and Maria Giannoudaki and Eric Fosler-Lussier and Alexandros Potamianos},
  title = {Associative and Semantic Features Extracted From Web-Harvested Corpora},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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