Semisupervised Learning for Computational Linguistics by Steven Abney

By Steven Abney

We are ultimately attending to the purpose the place Computational Linguistics will begin to see their titles within the titles. long ago one must piggyback off of one other self-discipline to get the knowledge they wanted. This booklet is a needs to for a person studying something statistical within the NLP box. I took a category which coated the majority of the subjects during this e-book simply months ahead of the ebook got here out. I struggled via the various recommendations and spent many a sleepless evening going over an educational paper at the least yet one more time getting these suggestions down. at the final day of sophistication the professor advised this new name. I went and purchased and many of the difficult stuff I had struggled with solidified in my brain. a good feeling! I want it was once the textbook.
concerning the ebook itself; it does imagine the reader is beautiful math savvy. a few sections declare they aren't breaking down an explanation even supposing the one factor at the web page are equations. yet at the turn facet, Abney does an attractive task of grounding the terminology prior to launching into that. the 1st few chapters are very informative and sufferer with the reader. it's also very good when you simply desire a refresher on any of those subject matters.

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Among the rules that match a given instance, the decision list’s prediction is determined by the one that has the highest score. Note that a decision list actually provides more information than just a prediction concerning the label for a given input instance. The score of the 18 Semisupervised Learning for Computational Linguistics “winning” rule can be interpreted as a measure of confidence: the higher the score, the more confident the classifier is that its prediction is correct. A classifier such as this, that produces both a label prediction and also a measure of confidence in that prediction, is said to be a confidence-rated classifier.

The word-sense disambiguation task makes some special constraints available that can be used to improve learning. It has been observed [91] that a given word occurring multiple times in a single discourse (for example, in the same newspaper article) is almost always translated the same way each time it occurs, even though multiple translations are possible in principle. Yarowsky incorporated this constraint into self-training for word-sense disambiguation. An important empirical question is whether semisupervised learning is effective, that is, whether it affords improvements over using just the labeled data.

Information extraction typically involves a small number of types of semantic entities and relations, corresponding to the schema of the database to be populated. The classic example is the “acquisitions and mergers” domain of the Message Understanding Conference (MUC), which assumes entity types such as person, place, and company, and relations such as person is-CEO-of company, company acquires company, etc. Entity recognition is the identification of phrases (usually noun phrases) referring to semantic entities.

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