Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification

This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution. MWE plays an important role in the applications of Natural Language Processing(NLP) like Machine Translation, Part of Speech tagging, Information Retrieval, Question Answering etc. Feature selection is an important factor in the recognition of Manipuri MWEs using Conditional Random Field (CRF). The disadvantage of manual selection and choosing of the appropriate features for running CRF motivates us to think of Genetic Algorithm (GA). Using GA we are able to find the optimal features to run the CRF. We have tried with fifty generations in feature selection along with three fold cross validation as fitness function. This model demonstrated the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%, showing an improvement over the CRF based Manipuri MWE identification without GA application.Comment: 14 pages, 6 figures, see http://airccse.org/journal/jcsit/1011csit05.pd

Improve and Implement an Open Source Question Answering System

A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when it comes to language structure, characters etc. We have implemented the Question Answer System so that it supports localization and improved Part of Speech tagging performance by storing the lexicon in the database instead of a file based lexicon. We have implemented a brill tagger variant for Part of Speech tagging of Hindi phrases and grammar rules for triplet extraction. We also improve Yioop’s lexical data handling support by allowing the user to add named entities. Our improvements to Yioop were then evaluated by comparing the retrieved answers against a dataset of answers known to be true. The test data for the question answering system included creating 2 indexes, 1 each for English and Hindi. These were created by configuring Yioop to crawl 200,000 wikipedia pages for each crawl. The crawls were configured to be domain specific so that English index consists of pages restricted to English text and Hindi index is restricted to pages with Hindi text. We then used a set of 50 questions on the English and Hindi systems. We recored, Hindi system to have an accuracy of about 55% for simple factoid questions and English question answer system to have an accuracy of 63%