Download or read book Large Scale Distributed Semantic N gram Language Model written by Yuandong Jiang and published by . This book was released on 2011 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Language model is a crucial component in statistical machine translation system. The basic language model is N-gram which predicts the next word based on previous N-1 words. It has been used in the state-of-the-art commercial machine translation systems over years. However, the N-gram model ignores the rich syntactic and semantic structure in natural languages. We propose a composite semantic N-gram language model which combines probabilistic latent semantic analysis model with N-gram as a generative model. We have implemented the proposed composite language model in a super-computer with thousand processors that is trained by 1.3 billion tokens corpus. Comparing with simple N-gram, the large scale composite language model has achieved significant perplexity reduction and BLEU score improvement in an n-best list re-ranking task for machine translation.

Download or read book A Large Scale Distributed Syntactic Semantic and Lexical Language Model for Machine Translation written by Ming Tan and published by . This book was released on 2013 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The n-gram model is the most widely used language model (LM) in statistical machine translation system, due to its simplicity and scalability. However, it only encodes the local lexical relation between adjacent words and clearly ignores the rich syntactic and semantic structures of the natural languages. Attempting to increase the order of an n-gram to describe longer range dependencies in natural language immediately runs into the curse of dimensionality. Although previous researches tried to increase the order of n-gram on a large corpus, they did not see obvious improvement beyond 6-gram. Meanwhile, other LMs, such as syntactic language models and topic language models, tried to encode the long range dependencies from different perspectives of natural languages. But it is still an open question how to effectively combine those language models in order to capture multiple linguistic phenomena. This dissertation presents a study at building a large scale distributed composite language model that is formed by seamlessly combining an n-gram model, a structured language model, and probabilistic latent semantic analysis under a directed Markov random field paradigm to simultaneously account for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm and a follow-up EM algorithm. To improve word prediction power, the composite LM is distributed with client-server paradigm and trained on corpora with up to a billion tokens. Also, the orders of the composite LM are increased up to 5-gram and 4-headword. The large scale distributed composite language model gives drastic perplexity reduction over n-grams and achieves significantly better translation quality measured by the BLEU score and "readability" of translations when applied to the task of re-ranking the N-best list from a state-of-the-art parsing-based machine translation system. Moreover, we propose an A*-search-based lattice rescoring strategy in order to integrate the large scale distributed composite language model into a phrase-based machine translation system. Experiments show that the A*-based lattice re-scoring is more effective to show the predominance of the composite language model over the n-gram model than the traditional N-best list re-scoring.

Download or read book Large Scale Distributed Syntactic Semantic and Lexical Language Models written by Shaojun Wang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A composite language model may include a composite word predictor. The composite word predictor may include a first language model and a second language model that are combined according to a directed Markov random field. The composite word predictor can predict a next word based upon a first set of contexts and a second set of contexts. The first language model may include a first word predictor that is dependent upon the first set of contexts. The second language model may include a second word predictor that is dependent upon the second set of contexts. Composite model parameters can be determined by multiple iterations of a convergent N-best list approximate Expectation-Maximization algorithm and a follow-up Expectation-Maximization algorithm applied in sequence, wherein the convergent N-best list approximate Expectation-Maximization algorithm and the follow-up Expectation-Maximization algorithm extracts the first set of contexts and the second set of contexts from a training corpus.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Download or read book Mobile Speech and Advanced Natural Language Solutions written by Amy Neustein and published by Springer Science & Business Media. This book was released on 2013-02-03 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mobile Speech and Advanced Natural Language Solutions" presents the discussion of the most recent advances in intelligent human-computer interaction, including fascinating new study findings on talk-in-interaction, which is the province of conversation analysis, a subfield in sociology/sociolinguistics, a new and emerging area in natural language understanding. Editors Amy Neustein and Judith A. Markowitz have recruited a talented group of contributors to introduce the next generation natural language technologies for practical speech processing applications that serve the consumer’s need for well-functioning natural language-driven personal assistants and other mobile devices, while also addressing business’ need for better functioning IVR-driven call centers that yield a more satisfying experience for the caller. This anthology is aimed at two distinct audiences: one consisting of speech engineers and system developers; the other comprised of linguists and cognitive scientists. The text builds on the experience and knowledge of each of these audiences by exposing them to the work of the other.

Download or read book Speech Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Chinese Lexical Semantics written by Jia-Fei Hong and published by Springer Nature. This book was released on 2020-01-03 with total page 873 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the 20th Chinese Lexical Semantics Workshop, CLSW 2019, held in Chiayi, Taiwan, in June 2019. The 39 full papers and 46 short papers included in this volume were carefully reviewed and selected from 254 submissions. They are organized in the following topical sections: lexical semantics; applications of natural language processing; lexical resources; corpus linguistics.

Download or read book Natural Language Understanding and Intelligent Applications written by Chin-Yew Lin and published by Springer. This book was released on 2016-11-30 with total page 963 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and the 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, held in Kunming, China, in December 2016. The 48 revised full papers presented together with 41 short papers were carefully reviewed and selected from 216 submissions. The papers cover fundamental research in language computing, multi-lingual access, web mining/text mining, machine learning for NLP, knowledge graph, NLP for social network, as well as applications in language computing.

Download or read book RoCKIn written by Multiple Authors and published by BoD – Books on Demand. This book was released on 2017-08-09 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book "RoCKIn - Benchmarking Through Robot Competitions" describes the activities and achievements on the promotion of Robotics research and benchmarking in Europe through robot competitions, carried out within the framework of the RoCKIn ("Robot Competitions Kick Innovation in Cognitive Systems and Robotics") Coordination Action, a project funded by the European Commission (EC) 7th Framework Programme (FP7). RoCKIn was one of the two pioneer projects on robot competitions in Europe funded by the EC, representing the acknowledgment of robot competitions as important tools to advance research on Robotics, besides education and public awareness of Robotics. Two challenges were selected for the RoCKIn competitions due to their high relevance and impact on Europe's societal and industrial needs: domestic service robots (RoCKIn@Home) and innovative robot applications in industry (RoCKIn@Work). Along the book chapters the reader will find details on RoCKIn@Home and RoCKIn@Work, and about the activities carried out during the project lifetime, namely the developed open domain test beds for competitions targeting the two challenges and usable by researchers worldwide; the scoring and benchmarking methods to assess the performance of robot systems and subsystems; and the building up of a community of new teams. The book ends with an assessment by the project industrial partner about the impact of RoCKIn and other robot competitions on the industrial robot markets. The project work was funded by the European Commission (EC) 7th Framework Programme (FP7), under the 9th Call for projects on Information and Communication Technologies. The publishing of this book was funded by the EC FP7 Post-Grant Open Access Pilot programme.

Download or read book Empirical Evidences and Theoretical Assumptions in Functional Linguistics written by Elissa Asp and published by Taylor & Francis. This book was released on 2022-07-28 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection explores the relationships between theory and evidences in functional linguistics, bringing together perspectives from both established and emerging scholars. The volume begins by establishing theoretical common ground for functional approaches to language, critically discussing empirical inquiry in functional linguistics and the challenges and opportunities of using new technologies in linguistic investigations. Building on this foundation, the second part of the volume explores the challenges involved in using different data sources as evidence for theorizing language and linguistic processes, drawing on work on lexical cohesion in language variation, neuroimaging and neuropathological data, and keystroke logging and eye-tracking. The final section of the volume examines the ways in which evidences from a wide range of data sources can offer new perspectives toward challenging established theoretical claims, employing empirical evidences from corpus linguistic analysis, keystroke logging, and multimodal communication. This pioneering collection synthesizes perspectives and addresses fundamental questions in the investigation of the relationships between theory and evidences in functional linguistics and will be of particular interest to researchers working in the field, as well as linguists working in experimental and interdisciplinary approaches which seek to bridge this gap.

Download or read book Advances in Knowledge Discovery and Data Mining written by Hady W. Lauw and published by Springer Nature. This book was released on 2020-05-08 with total page 936 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

Download or read book Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data written by Maosong Sun and published by Springer. This book was released on 2014-09-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th China National Conference on Computational Linguistics, CCL 2014, and of the First International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2014, held in Wuhan, China, in October 2014. The 27 papers presented were carefully reviewed and selected from 233 submissions. The papers are organized in topical sections on word segmentation; syntactic analysis and parsing the Web; semantics; discourse, coreference and pragmatics; textual entailment; language resources and annotation; sentiment analysis, opinion mining and text classification; large‐scale knowledge acquisition and reasoning; text mining, open IE and machine reading of the Web; machine translation; multilinguality in NLP; underresourced languages processing; NLP applications.

Download or read book Computational approaches to semantic change written by Nina Tahmasebi and published by Language Science Press. This book was released on 2021-08-30 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned knowledge and expertise of traditional historical linguistics with cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge. The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems — e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives.

Download or read book Continuous Space Models with Neural Networks in Natural Language Processing written by Hai Son Le and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of language models is in general to capture and to model regularities of language, thereby capturing morphological, syntactical and distributional properties of word sequences in a given language. They play an important role in many successful applications of Natural Language Processing, such as Automatic Speech Recognition, Machine Translation and Information Extraction. The most successful approaches to date are based on n-gram assumption and the adjustment of statistics from the training data by applying smoothing and back-off techniques, notably Kneser-Ney technique, introduced twenty years ago. In this way, language models predict a word based on its n-1 previous words. In spite of their prevalence, conventional n-gram based language models still suffer from several limitations that could be intuitively overcome by consulting human expert knowledge. One critical limitation is that, ignoring all linguistic properties, they treat each word as one discrete symbol with no relation with the others. Another point is that, even with a huge amount of data, the data sparsity issue always has an important impact, so the optimal value of n in the n-gram assumption is often 4 or 5 which is insufficient in practice. This kind of model is constructed based on the count of n-grams in training data. Therefore, the pertinence of these models is conditioned only on the characteristics of the training text (its quantity, its representation of the content in terms of theme, date). Recently, one of the most successful attempts that tries to directly learn word similarities is to use distributed word representations in language modeling, where distributionally words, which have semantic and syntactic similarities, are expected to be represented as neighbors in a continuous space. These representations and the associated objective function (the likelihood of the training data) are jointly learned using a multi-layer neural network architecture. In this way, word similarities are learned automatically. This approach has shown significant and consistent improvements when applied to automatic speech recognition and statistical machine translation tasks. A major difficulty with the continuous space neural network based approach remains the computational burden, which does not scale well to the massive corpora that are nowadays available. For this reason, the first contribution of this dissertation is the definition of a neural architecture based on a tree representation of the output vocabulary, namely Structured OUtput Layer (SOUL), which makes them well suited for large scale frameworks. The SOUL model combines the neural network approach with the class-based approach. It achieves significant improvements on both state-of-the-art large scale automatic speech recognition and statistical machine translations tasks. The second contribution is to provide several insightful analyses on their performances, their pros and cons, their induced word space representation. Finally, the third contribution is the successful adoption of the continuous space neural network into a machine translation framework. New translation models are proposed and reported to achieve significant improvements over state-of-the-art baseline systems.

Download or read book Advances in Neural Information Processing Systems 17 written by Lawrence K. Saul and published by MIT Press. This book was released on 2005 with total page 1710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.

Download or read book Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data written by Maosong Sun and published by Springer. This book was released on 2013-10-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th China National Conference on Computational Linguistics, CCL 2013, and of the First International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2013, held in Suzhou, China, in October 2013. The 32 papers presented were carefully reviewed and selected from 252 submissions. The papers are organized in topical sections on word segmentation; open-domain question answering; discourse, coreference and pragmatics; statistical and machine learning methods in NLP; semantics; text mining, open-domain information extraction and machine reading of the Web; sentiment analysis, opinion mining and text classification; lexical semantics and ontologies; language resources and annotation; machine translation; speech recognition and synthesis; tagging and chunking; and large-scale knowledge acquisition and reasoning.

Download or read book Syntactic N grams in Computational Linguistics written by Grigori Sidorov and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about a new approach in the field of computational linguistics related to the idea of constructing n-grams in non-linear manner, while the traditional approach consists in using the data from the surface structure of texts, i.e., the linear structure. In this book, we propose and systematize the concept of syntactic n-grams, which allows using syntactic information within the automatic text processing methods related to classification or clustering. It is a very interesting example of application of linguistic information in the automatic (computational) methods. Roughly speaking, the suggestion is to follow syntactic trees and construct n-grams based on paths in these trees. There are several types of non-linear n-grams; future work should determine, which types of n-grams are more useful in which natural language processing (NLP) tasks. This book is intended for specialists in the field of computational linguistics. However, we made an effort to explain in a clear manner how to use n-grams; we provide a large number of examples, and therefore we believe that the book is also useful for graduate students who already have some previous background in the field.