PPT-Named Entity Recognition in Query

Author : karlyn-bohler | Published Date : 2018-03-13

Jiafeng Guo 1 Gu Xu 2 Xueqi Cheng 1 Hang Li 2 1 Institute of Computing Technology CAS China 2 Microsoft Research Asia China Outline Problem Definition Potential

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Named Entity Recognition in Query: Transcript

Jiafeng Guo 1 Gu Xu 2 Xueqi Cheng 1 Hang Li 2 1 Institute of Computing Technology CAS China 2 Microsoft Research Asia China Outline Problem Definition Potential Applications. assumptions. For example:- In “Centaur Science Group, 1513 28th St NW, Washington, DC 20007 USA. (PubMed ID: 16796054)”, Centaur Science Group is an independent company that doesn’t con Martin . Ja. čala and . Jozef Tvarožek. Špindlerův. . Mlýn. , Czech Republic. January 23, 2012. Slovak University of Technology. Bratislava, Slovakia. Problem. Given an input text, detect and decide on correct meaning of named entites. Gu Xu. Microsoft Research Asia. Mismatching Problem. Mismatching is Fundamental Problem in Search. Examples:. NY ↔ New York, game cheats ↔ game . cheatcodes. Search Engine Challenges. Head or frequent queries. TwitterNLP. Ludymila Lobo . Twitter NLP. Ludymila. Lobo. Reading material. Named . Entity Recognition in Tweets, . RITTER, Alan, CLARK, Sam, . Mausam. and . ETZIONI, Oren. Obtained . on . Association . to improve. Machine Translation. . Neeraj Agrawal. Ankush. . Singla. Named Entities (. NEs. ). Important Part of a Sentence. No special treatment in current MT systems. Results in dropping or mistranslations. WITH . RANDOM FORESTS AND. BAYESIAN OPTIMIZATION. Presenters: . Arni. , . Sanjana. Named Entity Recognition. Subtask of Information Extraction. Identify known entity names – person, places, organization etc. Sujan. Perera. 1. , Pablo Mendes. 2. , Amit Sheth. 1. , . Krishnaprasad. Thirunarayan. 1. , . Adarsh. Alex. 1. , Christopher Heid. 3. , Greg Mott. 3. 1. Kno.e.sis Center, Wright State University, . Sujan. Perera. 1. , Pablo Mendes. 2. , Amit Sheth. 1. , . Krishnaprasad. Thirunarayan. 1. , . Adarsh. Alex. 1. , Christopher Heid. 3. , Greg Mott. 3. 1. Kno.e.sis Center, Wright State University, . Real-Time Exploitation of Linked Data. University of Crete. Computer Science . Department. Greece . Foundation for Research and Technology – Hellas (FORTH). Institute of Computer Science (ICS). Information Systems Laboratory (ISL). Julie . Lerman. The Data Farm. jlerman@thedatafarm.com. DTL312 . Julie Lerman. Mentor/Consultant. jlerman@thedatafarm.com. thedatafarm.com/blog. LearnEntityFramework.com. 10 Tips & Tricks . Solving Pain Points. Tim Teaching. Information Extraction. Information extraction (IE) systems. Menemukan. . dan. . memahami. . bagian. . tertentu. yang . relevan. . dalam. . teks. yang . tidak. . terstruktur. Mengumpulkan. Introducing the tasks:. Getting simple structured information out of text. Information Extraction. Information extraction . (IE) systems. Find and understand limited relevant parts of . texts. Gather information from many pieces of text. Tim Teaching. Information Extraction. Information extraction (IE) systems. Menemukan. . dan. . memahami. . bagian. . tertentu. yang . relevan. . dalam. . teks. yang . tidak. . terstruktur. Mengumpulkan. Named Entity Recognition in Tweets: TwitterNLP Ludymila Lobo Twitter NLP Ludymila Lobo Reading material Named Entity Recognition in Tweets, RITTER, Alan, CLARK, Sam, Mausam and ETZIONI, Oren. Obtained

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