Improving an Open Source Question Answering System
This project aims to integrate a Question Answering System into Yioop for improved user interaction. The focus is on creating a robust part-of-speech tagger for languages like Hindi, Marathi, etc. Learn about the components and architecture of QA systems, including lexicons, parts-of-speech taggers, and parse tree creation.
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Presentation Transcript
Improving an Open Source Question Answering System - Salil Shenoy
Outline Problem Statement What is a Question Answering System ? Components of the Question Answering System 1. Lexicon 2. Parts of Speech Tagger 3. Parse Tree Creation 4. Triplet Extraction Process
Problem Statement When the user enters any question in Yioop it should be able to return the answer in natural language. Currently, Yioop treats a question and a query in the same way by returning a list of documents. The main goals for the project 1. Integration of a Question Answering System in Yioop 2. Create a part of speech tagger for a particular language (for example Hindi, Marathi, etc.)
Question Answering [QA] System Discipline of Computer Science which involves answering questions in natural language Any QA system depends on a database to answer questions Short answers in natural language returned instead of list of documents Can be classified broadly into 1. Open Domain 2. Closed Domain
Components of the Question Answering System Following are the main components of any QA system Lexicon Sort of dictionary, contains list of words for a given language and Its corresponding part of speech Eg. abandoned VB JJ where VB is verb, JJ is adjective
Components of Question Answering System Parts of Speech Tagger Example Statement: The grand jury commented on a number of other topics Output of statement The[DT] grand[JJ] jury[NN] commented[VBD] on[IN] a[DT] number[NN] of[IN] other[JJ] topics[NNS]
Components of Question Answering System Parse Tree Grammar rules to interpret a common form of a statement NP: {<DT|JJ|NN.*>+} PP: {<IN><NP>} VP: {<VB.*><NP|PP>+} Statement: {<NP><VP>}
Components of the Question Answering System Triplet Extraction Algorithm Subject Extraction : Noun in the NP Sub-tree Predicate Extraction: Deepest Verb descendent of Verb Phrase Object Extraction : All siblings of VB in Verb Phrase Sub-tree
