In this paper, we motivate the need for a publicly available, generic software framework for question-answering (QA) systems. We present an open-source QA framework QANUS which researchers can leverage on to build new QA systems easily and rapidly. The framework implements much of the code that will otherwise have been repeated across different QA systems. To demonstrate the utility and practicality of the framework, we further present a fully functioning factoid QA system QA-SYS built on top of QANUS.
The architecture of is slanted towards QA systems based on current state-of-the-art information retrieval (IR) techniques. These techniques typically involve manipulating the lexical and syntactic form of natural language text and do not attempt to comprehend the semantics expressed by the text. Systems which make use of these techniques @cite_2 @cite_3 have been able to perform ahead of their peers in the Text Retrieval Conference (TREC) QA tracks @cite_6 .
@cite_6 @cite_3 @cite_2
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An electrically operated power steering device which is fitted to the steering system of a vehicle to provide supplementary steering power by means of an electric motor, wherein the electrically operated power steering device controls the motor current by the signal corresponding to the torsional torque in the steering system and the single corresponding to the restoring torque in accordance with the angular displacement of the wheel, so that the restoring force takes effect immediately to restore the steering wheel when the input torque on the steering wheel is reduced. Question Answering (QA) is retrieving answers to natural language questions from a collection of documents rather than retrieving relevant documents containing the keywords of the query which is performed by search engines. What a user usually wants is often a precise answer to a question. For example, given the question “Who won the nobel prize in peace in 2006?” what a user really wants is the answer “Dr. Muhammad Yunus”, in stead of reading through lots of documents that contain the words “win”, “nobel”,“prize”, “peace” and “2006” etc. This means that question answering systems will possibly be integral to the next generation of search engines. The Text Retrieval Conference, TREC1 QA track is the major large-scale evaluation environment for open-domain question answering systems. The questions in the TREC-2007 QA track are clustered by target, which is the overall theme or topic of the questions. The track has three types of questions: 1. factoid that require only one correct response, 2. list that require a non redundant list of correct responses and 3. other questions that require a non redundant list of facts about the target that has not already been discovered by a previous answer. We took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information (i.e. answer type) from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available tools to tag the documents. Our system classifies the questions using manually developed rules. In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10.