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Recognizing Textual Entailment
In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research com...
Recognizing Textual Entailment
As researchers try to build on existing research in Natural Language Processing (NLP), they are finding that Natural Language Understanding (NLU) is important not only in high-level tasks like semantic search, where the goal is to access concepts instead of keywords, but also in low-level semantic tagging tasks such as Named Entity Recognition. Understanding (to some extent) the context of terms of interest is needed to decide whether or not an entity/concept of interest is present, however the author phrased it. In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment. This task encapsulates Natural Language Understandi...
The Oxford Handbook of Computational Linguistics
Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.
Machine Learning Challenges
This book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Advances in Natural Language Processing
This book constitutes the refereed proceedings of the 5th International Conference on Natural Language Processing, FinTAL 2006, held in Turku, Finland in August 2006. The book presents 72 revised full papers together with 1 invited talk and the extended abstracts of 2 invited keynote addresses. The papers address all current issues in computational linguistics and monolingual and multilingual intelligent language processing - theory, methods and applications.
Machine Translation and the Information Soup
Machine Translation and the Information Soup! Over the past fty years, machine translation has grown from a tantalizing dream to a respectable and stable scienti c-linguistic enterprise, with users, c- mercial systems, university research, and government participation. But until very recently, MT has been performed as a relatively distinct operation, so- what isolated from other text processing. Today, this situation is changing rapidly. The explosive growth of the Web has brought multilingual text into the reach of nearly everyone with a computer. We live in a soup of information, an increasingly multilingual bouillabaisse. And to partake of this soup, we can use MT systems together with more and more tools and language processing technologies|information retrieval engines, - tomated text summarizers, and multimodal and multilingual displays. Though some of them may still be rather experimental, and though they may not quite t together well yet, it is clear that the future will o er text manipulation systems that contain all these functions, seamlessly interconnected in various ways.
Bitext Alignment
This book provides an overview of various techniques for the alignment of bitexts. It describes general concepts and strategies that can be applied to map corresponding parts in parallel documents on various levels of granularity. Bitexts are valuable linguistic resources for many different research fields and practical applications. The most predominant application is machine translation, in particular, statistical machine translation. However, there are various other threads that can be followed which may be supported by the rich linguistic knowledge implicitly stored in parallel resources. Bitexts have been explored in lexicography, word sense disambiguation, terminology extraction, compu...
Recent Advances in Natural Language Processing IV
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Semantics. Volume 3
No detailed description available for "SEMANTICS (MAIENBORN ET AL.) BD. 33.3 HSK E-BOOK".
Advances in Computational Intelligence
The two-volume set LNAI 7629 and LNAI 7630 constitutes the refereed proceedings of the 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, held in San Luis Potosí, Mexico, in October/November 2012. The 80 revised papers presented were carefully reviewed and selected from 224 submissions. The second volume includes 40 papers focusing on soft computing. The papers are organized in the following topical sections: natural language processing; evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid intelligent systems; fuzzy systems and probabilistic models in decision making.
