Synthesis Lectures on Human Language Technologies最新文献_第2页

Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics 自然语言处理的语言学基础II:语义学和语用学的100个要点

Synthesis Lectures on Human Language Technologies Pub Date : 2019-11-05 DOI: 10.2200/s00935ed1v02y201907hlt043

Emily M. Bender, A. Lascarides

引用次数: 18

Bayesian Analysis in Natural Language Processing, Second Edition 自然语言处理中的贝叶斯分析，第二版

Synthesis Lectures on Human Language Technologies Pub Date : 2019-04-08 DOI: 10.2200/S00905ED2V01Y201903HLT041

Shay B. Cohen

引用次数: 2

Neural Network Methods for Natural Language Processing 自然语言处理的神经网络方法

Synthesis Lectures on Human Language Technologies Pub Date : 2017-01-01 DOI: 10.2200/S00762ED1V01Y201703HLT037

Yoav Goldberg

引用次数: 547

Semi-Supervised Learning and Domain Adaptation in Natural Language Processing 自然语言处理中的半监督学习和领域自适应

Synthesis Lectures on Human Language Technologies Pub Date : 2013-05-21 DOI: 10.2200/s00497ed1v01y201304hlt021

Anders Søgaard

{"title":"Semi-Supervised Learning and Domain Adaptation in Natural Language Processing","authors":"Anders Søgaard","doi":"10.2200/s00497ed1v01y201304hlt021","DOIUrl":"https://doi.org/10.2200/s00497ed1v01y201304hlt021","url":null,"abstract":"This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees (\"this algorithm never does too badly\") than about useful rules of thumb (\"in this case this algorithm may perform really well\"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant. Table of Contents: Introduction / Supervised and Unsupervised Prediction / Semi-Supervised Learning / Learning under Bias / Learning under Unknown Bias / Evaluating under Bias","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"57 1","pages":"1-103"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73859718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Automated Grammatical Error Detection for Language Learners 自动语法错误检测语言学习者

Synthesis Lectures on Human Language Technologies Pub Date : 2010-05-11 DOI: 10.2200/S00275ED1V01Y201006HLT009

C. Leacock, M. Chodorow, Michael Gamon, Joel R. Tetreault

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引用次数: 223