Dissertations and Theses

Date of Award


Document Type


First Advisor

Jie Wei


Natural language processing, distributed representation, sentence completion


In recent years, the distributed representation of words in vector space or word embeddings have become very popular as they have shown significant improvements in many statistical natural language processing (NLP) tasks as compared to traditional language models like Ngram. In this thesis, we explored various state-of-the-art methods like Latent Semantic Analysis, word2vec, and GloVe to learn the distributed representation of words. Their performance was compared based on the accuracy achieved when tasked with selecting the right missing word in the sentence, given five possible options. For this NLP task we trained each of these methods using a training corpus that contained texts of around five hundred 19th century novels from Project Gutenberg. The test set contained 1040 sentences where one word was missing from each sentence. The training and test set were part of the Microsoft Research Sentence Completion Challenge data set. In this work, word vectors obtained by training skip-gram model of word2vec showed the highest accuracy in finding the missing word in the sentences among all the methods tested. We also found that tuning hyperparameters of the models helped in capturing greater syntactic and semantic regularities among words.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.