In this paper, we experiment with the task of contextual synonym expansion, and compare the benefits of combining multiple lexical resources using both unsupervised and supervised approaches. Overall, the results obtained through the combination of several resources exceed the current state-of-the-art when selecting the best synonym for a given target word, and place second when selecting the top ten synonyms, thus demonstrating the usefulness of the approach.

This report describes a variety of work that uses web-scale N-gram data. This

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We experiment with splitting words into their stem and suffix components for modeling morphologically rich languages. We show that using a morphological analyzer and disambiguator results in a significant perplexity reduction in Turkish. We present flexible n-gram models, Flex-Grams, which assume that the n−1 tokens that determine the probability of a given token can be chosen anywhere in the sentence rather than the preceding n − 1 positions. Our final model achieves 27 % perplexity reduction compared to the standard n-gram model. 1

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We introduce a new publicly available tool that implements efficient indexing and retrieval of large N-gram datasets, such as the Web1T 5-gram corpus. Our tool indexes the entire Web1T dataset with an index size of only 100 MB and performs a retrieval of any N-gram with a single disk access. With an increased index size of 420 MB and duplicate data, it also allows users to issue wild card queries provided that the wild cards in the query are contiguous. Furthermore, we also implement some of the smoothing algorithms that are designed specifically for large datasets and are shown to yield better language models than the traditional ones on the Web1T 5-gram corpus (Yuret, 2008). We demonstrate the effectiveness of our tool and the smoothing algorithms on the English Lexical Substitution task by a simple implementation that gives considerable improvement over a basic language model. 1

certifies that this is the approved version of the following dissertation: Word meaning in context as a paraphrase distribution: Evidence,