Learning Temporal-Dependent Ranking Models Miguel Costa, Francisco Couto, Mário Silva LaSIGE @ Faculty of Sciences, University of Lisbon IST INESC-ID, University of Lisbon 37th Annual ACM SIGIR Conference, Gold Coast, A - Walton, Milton Keynes - Document - PDFSEARCH.IO

First Page		Document Content
Date: 2014-09-03 11:43:47 Information science Information retrieval Web archiving Natural language processing WebCite Special Interest Group on Information Retrieval Learning to rank Web search engine Temporal information retrieval		Learning Temporal-Dependent Ranking Models Miguel Costa, Francisco Couto, Mário Silva LaSIGE @ Faculty of Sciences, University of Lisbon IST/INESC-ID, University of Lisbon 37th Annual ACM SIGIR Conference, Gold Coast, A Add to Reading List Source URL: xldb.fc.ul.pt Download Document from Source Website File Size: 1.003,07 KB Share Document on Facebook

	Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health Tim Althoff∗, Kevin Clark∗, Jure Leskovec Stanford University {althoff, kevclark, jure}@cs.stanford.edu DocID: 1xVji - View Document
	Empirical data on corpus design and usage in biomedical natural language processing DocID: 1vnzz - View Document
	Chapter 1 An Introduction to SA Sentiment Analysis (SA) refers to a broad field of study in Natural Language Processing (NLP) and Artificial Intelligence (AI). In general, Sentiment Analysis is the task of identifying th DocID: 1vjUA - View Document
	The Social and the Neural Network: How to Make Natural Language Processing about People again Dirk Hovy Bocconi University Via Roberto SarfattiMilan, MI, Italy DocID: 1v7Zu - View Document
	Automated Scoring of Chatbot Responses in Conversational Dialogue Steven Kester Yuwono, Wu Biao, Luis Fernando D’Haro Abstract Rapid advancement in natural language processing (NLP) and machine learning has led to the DocID: 1uXgQ - View Document