Metonymy is a figure of speech in which one item's name represents another item that usually has a close relation with the first one. Metonymic expressions need to be correctly detected and interpreted because sentences including such expressions have different mean- ings from literal ones; computer systems may output inappropriate results in natural language processing. In this paper, an associative approach for analyzing metonymic expressions is proposed. By using associative information and two conceptual distances between words in a sentence, a previous method is enhanced and a decision tree is trained to detect metonymic expressions. After detecting these expressions, they are interpreted as metonymic understanding words by using associative information. This method was evaluated by comparing it with two baseline methods based on previous studies on the Japanese language that used case frames and co-occurrence information. As a result, the proposed method exhibited significantly better accuracy (0.85) of determining words as metonymic or literal expressions than the baselines. It also exhibited better accuracy (0.74) of interpreting the detected metonymic expressions than the baselines.
@InProceedings{TERAOKA16.1116,
author = {Takehiro Teraoka}, title = {Metonymy Analysis Using Associative Relations between Words}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {may}, date = {23-28}, location = {Portorož, Slovenia}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {978-2-9517408-9-1}, language = {english} }
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