Analysis on Question Retrieval in Community Question Answering via NON-Negative Matrix Factorization

CQA helpful in answering real world question. CQA provide answer to human. Question retrieval in CQA can automatically �nd the most relevant and recent questions that have been solved by other users. We propose an alternative way to address the word ambiguity and word mismatch problems by taking advantage of potentially rich semantic information drawn from other languages. The translated words from other languages via non-negative matrix factorization. Contextual information is exploited during the translation from one language to another language by using Google Translate. Thus, word ambiguity can be solved based on the contextual information when questions are translated. Multiple words that have similar meanings in one language may be translated into a unique word or a few words in a foreign language. It is a word-based translation language model for retrieval with query likelihood model for answer. We use a translated representation by alternative enriching the original question with the words from other language in CQA. We translate the English question into other four language using Google translate which take into account contextual information during translation. If we translate the question word by word, it discard the contextual information. We would expect that such a translation would not be able to solve word ambiguity problem.