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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation


CHAPTER 6. AUTOMATIC EXTRACTION OF EXAMPLES FOR WSD 73 coarse fine 1 56.2 47.5 MFS 64.5 55.2 2 66.7 60.2 IAA 67.3 - htsa3 79.3 72.9 3 79.3 75.1 4 79.4 75.0 Table 6.16: Comparison of our system’s scores with the upper and lower bound for the experiment and also with the best performing system in the Senseval-3 English lexical sample task. Table 6.16 shows that our system performs best on the given task by attempting a semi- supervised approach. The latter employs the given by the Senseval-3 English lexical sample task training set that we extended with 141 automatically annotated instances. It outperforms both the upper and the lower bound for the given task and as well the best performing supervised system in the Senseval-3 English lexical sample task.

Chapter 7 Conclusion, Future and Related Work 7.1 Related Work There have been several approaches that relate to our work in the sense that they approach the automatic acquisition of sense-tagged corpora and its further usage in word sense disambigua- tion. Those attempts are described in more detail in (Gonzalo and Verdejo, 2007). The authors classify the strategies in five different types: 1. Acquisition by direct web searching 2. Bootstrapping from seed examples 3. Acquisition via web directories 4. Acquisition via cross-language evidence 5. Web-based cooperative annotation We will not aim to discuss in depth any of those approaches but rather note some of the conclusions made by Gonzalo and Verdejo (2007) about their general nature, which relates to the observed by us importance of the automatically acquired sense-tagged data. One such remark is the fact that similar to our experience, the quality of the automatically gathered data equals, or even outperforms the performance of the human-tagged examples. Another relevant issue that the authors mention is the correlation between the correctness of the annotation of the examples and the final performance of the system. Of course, it is the case that if the instances are correctly labeled normally the results will be better, however, there are cases in which the performance of the supervised system is very close to the performance of the unsupervised one. This means that not always the selected examples are useful for training a classifier. 74

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