PPT-Information Retrieval and Question-Answering

Julia Hirschberg CS 4705 Today Information Retrieval Review of Methods TREC IR Tracks Question Answering Factoid QA A Sample System UT Dallas Harabagiu A simpler alternative from MSR Information Retrieval. iaaccn Abstract Community question answering cQA which pro vides a platform for people with diverse back ground to share information and knowledge has become an increasingly popular research topic In this paper we focus on the task of question re tri What is Question Answering?. 2. Question Answering. One of the oldest NLP tasks (punched card systems in 1961). Simmons, Klein, . McConlogue. . 1964. Indexing and Dependency Logic for Answering English Questions. American Documentation 15:30, 196-204. Shallow & Deep. Techniques for NLP. Ling571. Deep Processing Techniques for NLP. March 9, 2011. Examples from Dan . Jurafsky. ). Roadmap. Question-Answering: . Definitions & Motivation. Basic pipeline:. Introduction. Ling 573. NLP Systems and Applications. March 29, 2011. Roadmap. Motivation. 573 Structure. Question-Answering. Shared Tasks. Motivation. Information retrieval is very powerful. Search engines index and search enormous doc sets. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Overview. Ling573. Systems & Applications. April 4. , 2013. Roadmap. Dimensions of the problem. A (very) brief history. Architecture of a QA system. QA and resources. Evaluation. Challenges. Logistics Check-in. and Re-ranking. Ling573. NLP Systems and Applications. May 3, 2011. Upcoming Talks. Edith Law. Friday: 3:30; CSE 303. Human Computation: Core Research Questions and Opportunities . Games with a purpose, . Introduction. Ling 573. NLP Systems and Applications. April 1, 2013. Roadmap. Motivation. 573 Structure. Question-Answering. Shared Tasks. Motivation. Information retrieval is very powerful. Search engines index and search enormous doc sets. Lin Ma, . Zhengdong. Lu, and Hang Li. Huawei Noah’s Ark Lab, Hong Kong. http://. www.ee.cuhk.edu.hk. /~lma. /. . Mine the relationships between multiple modalities. Association different modalities. ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . Kai-Wei Chang. CS @ University of Virginia. kw@kwchang.net. Couse webpage: . http://kwchang.net/teaching/NLP16. 1. CS6501-NLP. Question answering. CS6501– Natural Language Processing. 2. credit: ifunny.com. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . (including a sketch of Information Retrieval. ). Slides adapted from Dan . Jurafsky. , . Jim . Martin and Ed . Hovy. Web-based Question . Answering. Information Retrieval (briefly. ). Today. The notion of getting computers to give reasonable answers to questions has been around for quite awhile.