natural language processing using (78 Ergebnisse)

Zustand: very good. Cambridge, MA: The MIT Press, 1994. Paperback. 359 pp.(ACL-MIT Series in Natural Language Processing). -Today, large corpora consisting of hundreds of millions or even billions of words, along with new empirical and statistical methods for organizing and analyzing these data, promise new insights into the use of language. Already, the data extracted from these large corpora reveal that language use is more flexible and complex than most rule-based systems have tried to account for, providing a basis for progress in the performance of Natural Language Processing systems. Using Large Corpora identifies these new data-oriented methods and describes the potential results that the use of large corpora offers.The research described shows that the new methods may offer solutions to key issues of acquisition (automatically identifying and coding information), coverage (accounting for all of the phenomena in a given domain), robustness (accommodating "real data" that may be corrupt or not accounted for in the model), and extensibility (applying the model and data to a new domain, text, or problem). There are chapters on lexical issues, issues in syntax, and translation topics, as well discussions of the "statistics-based" vs. "rule-based" debate.ACL-MIT Series in Natural Language ProcessingEnglish text. Condition : very good. Condition : very good copy. ISBN 9780262510820. Keywords : LINGUISTICS,

Zustand: as new. Cambridge, MA : The MIT Press, 2010. Hardcover. Dustjacket. 512 pp.- The last decade has seen computational implementations of large hand-crafted natural language grammars in formal frameworks such as Tree-Adjoining Grammar (TAG), Combinatory Categorical Grammar (CCG), Head-driven Phrase Structure Grammar (HPSG), and Lexical Functional Grammar (LFG). Grammars in these frameworks typically associate linguistically motivated rich descriptions (Supertags) with words. With the availability of parse-annotated corpora, grammars in the TAG and CCG frameworks have also been automatically extracted while maintaining the linguistic relevance of the extracted Supertags. In these frameworks, Supertags are designed so that complex linguistic constraints are localized to operate within the domain of those descriptions. While this localization increases local ambiguity, the process of disambiguation (Supertagging) provides a unique way of combining linguistic and statistical information. This volume investigates the theme of employing statistical approaches with linguistically motivated representations and its impact on Natural Language Processing tasks. In particular, the contributors describe research in which words are associated with Supertags that are the primitives of different grammar formalisms including Lexicalized Tree-Adjoining Grammar (LTAG). ContributorsJens Bäcker, Srinivas Bangalore, Akshar Bharati, Pierre Boullier, Tomas By, John Chen, Stephen Clark, Berthold Crysmann, James R. Curran, Kilian Foth, Robert Frank, Karin Harbusch, Sa?a Hasan, Aravind Joshi,Vincenzo Lombardo, Takuya Matsuzaki, Alessandro Mazzei, Wolfgang Menzel, Yusuke Miyao, Richard Moot, Alexis Nasr, Günter Neumann, Martha Palmer, Owen Rambow, Rajeev Sangal, Anoop Sarkar, Giorgio Satta, Libin Shen, Patrick Sturt, Jun'ichi Tsujii, K. Vijay-Shanker, Wen Wang, Fei XiaEnglish text. Condition : a Condition : as new copy. ISBN 9780262013871. Keywords : ,

Paperback. Zustand: Very Good. Text Generation: Using Discourse Strategies and Focus Constraints to Generate Natural Language Text (Studies in Natural Language Processing) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

Kartoniert / Broschiert. Zustand: New. Yuli Vasiliev is a programmer, freelance writer, and consultant who specializes in open source development, Oracle database technologies, and natural language processing.An introduction to natural language processing with Python using spaCy, a.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9781484240953.

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Taschenbuch. Zustand: Neu. Neuware -Academic Paper from the year 2018 in the subject Computer Science - Applied, University of the Witwatersrand, course: Machine learning - Artificial Intelligence - Big Data - Natural Language Processing, language: English, abstract: Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data, be them structured or unstructured, real-time or historical, the so-called Big Data. Publicly available cloud computing platforms have been made available, for instance, Amazon EC2, EMR, and Google Compute Engine. More importantly, open source APIs and libraries have also been developed for ease of programming on the cloud, for instance, Cascading, Storm, Scalding, Apache Spark and Trackur. Meanwhile, computational intelligence approaches, examples of which include evolutionary computation, immune-inspired approaches, and swarm intelligence, are also employed to develop scalable machine learning and data analytics tools.In this project, we presented the sentiment-focused web crawling problem and designed a sentiment-focused web crawler frame-work for faster discovery and retrieval of sentimental context on the Web. We have developed a computational framework to perform automated reputation analysis on the Web using Natural Language Processing and Machine Learning. This paper introduces such framework and tests its performance on automated sentiment analysis for brand reputation. In addition, we proposed different strategies for predicting the polarity scores of web pages.Experiments have shown that the performance of our proposed framework is more efficient than existing frameworks. Reputation analysis is a useful application for organizations that are looking for people's opinions about their products and services.Our approach consists of 4 parts: in the first part, the framework performed Web crawling based on the query specified by the user. In the second part, the framework locates relevant information within textual data using Entity Recognition. In the third part, relevant information was recorded in the database for feature extraction/engineering and classification. Lastly, the framework displayed the data for reputation analysis. In the training phase, we used data provided by the marketing team of the University of the Witwatersrand, Emoticons, a subset of the SentiStrength lexicon and ClueWeb09 dataset. Each domain was labelled accordingly (positive/negative and neutral) with equal numbers of polarity in plain text. In the test phase, the classifier predicted the polarity of real-time data. We used accuracy as evaluation metric to measure how much our classifier acted precisely.BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt 60 pp. Englisch.

Zustand: Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 277 | Sprache: Englisch | Produktart: Bücher.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 336 | Sprache: Englisch | Produktart: Bücher.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive overview of methods to build comparable corporaand of their applications, including machine translation, cross-lingual transfer, andvarious kinds of multilingual natural language processing. The authors begin witha brief history on the topic followed by a comparison to parallel resources and anexplanation of why comparable corpora have become more widely used. In particular,they provide the basis for the multilingual capabilities of pre-trained models, suchas BERT or GPT. The book then focuses on building comparable corpora, aligningtheir sentences to create a database of suitable translations, and using these sentencetranslations to produce dictionaries and term banks. Then, it is explained howcomparable corpora can be used to build machine translation engines and to developa wide variety of multilingual applications.

Taschenbuch. Zustand: Neu. Neuware -Scientific Study from the year 2018 in the subject Computer Sciences - Artificial Intelligence, grade: 1, Post Graduate Government College, language: English, abstract: Every natural language contains a large number of words. These words can have different senses in different context; such words with multiple senses are known as sense tagged words. Word sense reflects the basic concept of the word and the words with several meanings cause ambiguity in the sentence, and the process that decides which of the denotation is accurate in the sentence among several meanings of the word is known as Word Sense Disambiguation. Human beings are good at understanding the meaning of the word by reading the sentence but the same task is difficult for a machine: to understand and accurately sense the correct meaning of the word. Machines can easily understand the set of rules and it is a difficult task to create such rules that can easily disambiguate the word in the context. This task is complicated because every natural language has their own set of rules such as grammatical rules, part-of-speech, antonomy, and synonym. Therefore, a machine is trained by special algorithm so that it can tag the word with its correct sense. If the correct sense of the word is determined, that correct sense is helpful in retrieving the basic concepts of the word. As such this is very difficult task for a machine to retrieve the basic definition of word. In this proposed work, K-Nearest Neighbor (KNN) approach is used to disambiguate the sense tagged words. The KNN is based on supervised learning method. The proposed technique evaluates the performance on Hindi sense tagged words and these are obtained from Hindi Wordnet. The results show the effectiveness of the proposed technique in sense tagged words.BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt 88 pp. Englisch.

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Taschenbuch. Zustand: Neu. Neuware -This book is useful for the research persons, especially computational, educational, Linguistic, Natural language processing, computer Language translation, Operating system conversion & Translation. This book is very essential for Internet Technology script Translation including the software developing HUBs.A dialect 'KOKBOROK' language is used to make understand the reader as an example. One can simply change any language to make the best use of this book.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch.

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Zustand: Gut. Zustand: Gut | Seiten: 264 | Sprache: Englisch | Produktart: Bücher.

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