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Natural language processing (Computer science) --- Social media

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Peer reviewed articles from the Natural Language Processing and Cognitive Science (NLPCS) 2014 meeting in October 2014 workshop. The meeting fosters interactions among researchers and practitioners in NLP by taking a Cognitive Science perspective. Articles cover topics such as artificial intelligence, computational linguistics, psycholinguistics, cognitive psychology and language learning.

Natural language processing (Computer science) --- Cognitive science --- Computational linguistics --- NLP, cognitive science, computational linguistics, artificial intelligence.

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Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work.

Artificial intelligence. --- Natural language processing (Computer science). --- Computational linguistics. --- Artificial Intelligence. --- Natural Language Processing (NLP). --- Computational Linguistics.

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Compilers (Computer programs) --- Computer science --- Natural language processing (Computer science) --- Compilateurs (Logiciels) --- Informatique --- Traitement automatique des langues naturelles --- Textbooks --- Mathematics. --- Manuels d'enseignement supérieur --- Mathématiques

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This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.

Linguistics. --- Computational Linguistics. --- Computational linguistics. --- Linguistique --- Linguistique informatique --- Dependency grammar. --- Grammar, Comparative and general -- Parsing. --- Mathematical linguistics. --- Natural language processing (Computer science). --- Languages & Literatures --- Philology & Linguistics --- Natural language processing (Computer science) --- Grammar, Comparative and general --- Parsing. --- Valence (Linguistics) --- Parsing (Grammar) --- NLP (Computer science) --- Algebraic linguistics --- Language and languages --- Linguistics --- Linguistics, Mathematical --- Statistical methods --- Mathematical models --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Mathematical linguistics --- Applied linguistics --- Information theory --- Computational linguistics --- Syntax --- Automatic language processing --- Language data processing --- Natural language processing (Linguistics) --- Cross-language information retrieval --- Multilingual computing --- Data processing