Pub Date : 2023-01-01DOI: 10.1007/978-3-031-31384-4
S. Sharoff, R. Rapp, Pierre Zweigenbaum
{"title":"Building and Using Comparable Corpora for Multilingual Natural Language Processing","authors":"S. Sharoff, R. Rapp, Pierre Zweigenbaum","doi":"10.1007/978-3-031-31384-4","DOIUrl":"https://doi.org/10.1007/978-3-031-31384-4","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72659358","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}
Pub Date : 2022-01-05DOI: 10.2200/s01131ed1v01y202109hlt054
Silviu Paun, Ron Artstein, Massimo Poesio
{"title":"Statistical Methods for Annotation Analysis","authors":"Silviu Paun, Ron Artstein, Massimo Poesio","doi":"10.2200/s01131ed1v01y202109hlt054","DOIUrl":"https://doi.org/10.2200/s01131ed1v01y202109hlt054","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87105167","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}
Pub Date : 2021-12-03DOI: 10.2200/s01137ed1v01y202110hlt055
S. Riezler, M. Hagmann
{"title":"Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science","authors":"S. Riezler, M. Hagmann","doi":"10.2200/s01137ed1v01y202110hlt055","DOIUrl":"https://doi.org/10.2200/s01137ed1v01y202110hlt055","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"385 1","pages":"1-165"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79737329","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}
Pub Date : 2021-11-12DOI: 10.2200/s01121ed1v01y202108hlt052
Beata Beigman Klebanov, N. Madnani
{"title":"Automated Essay Scoring","authors":"Beata Beigman Klebanov, N. Madnani","doi":"10.2200/s01121ed1v01y202108hlt052","DOIUrl":"https://doi.org/10.2200/s01121ed1v01y202108hlt052","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"106 1","pages":"1-314"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91317817","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}
Pub Date : 2021-09-21DOI: 10.2200/s01118ed1v01y202107hlt051
Anders Søgaard
{"title":"Explainable Natural Language Processing","authors":"Anders Søgaard","doi":"10.2200/s01118ed1v01y202107hlt051","DOIUrl":"https://doi.org/10.2200/s01118ed1v01y202107hlt051","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"45 1","pages":"1-123"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76578053","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}
Pub Date : 2021-05-26DOI: 10.2200/S01086ED1V01Y202104HLT050
Kyle Gorman, R. Sproat
Abstract Weighted finite-state transducers (WFSTs) are commonly used by engineers and computational linguists for processing and generating speech and text. This book first provides a detailed intr...
{"title":"Finite-State Text Processing","authors":"Kyle Gorman, R. Sproat","doi":"10.2200/S01086ED1V01Y202104HLT050","DOIUrl":"https://doi.org/10.2200/S01086ED1V01Y202104HLT050","url":null,"abstract":"Abstract Weighted finite-state transducers (WFSTs) are commonly used by engineers and computational linguists for processing and generating speech and text. This book first provides a detailed intr...","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"42 1","pages":"1-158"},"PeriodicalIF":0.0,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75633088","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}
Pub Date : 2021-04-07DOI: 10.2200/S01078ED2V01Y202002HLT049
Vivi Nastase, Stan Szpakowicz, Preslav Nakov, Diarmuid Ó Séagdha
Abstract Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and...
{"title":"Semantic Relations Between Nominals, Second Edition","authors":"Vivi Nastase, Stan Szpakowicz, Preslav Nakov, Diarmuid Ó Séagdha","doi":"10.2200/S01078ED2V01Y202002HLT049","DOIUrl":"https://doi.org/10.2200/S01078ED2V01Y202002HLT049","url":null,"abstract":"Abstract Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and...","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"75 1","pages":"1-234"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73003224","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}
Pub Date : 2020-11-12DOI: 10.2200/s01057ed1v01y202009hlt047
José Camacho-Collados, Mohammad Taher Pilehvar
Word vector representations have a long tradition in several research fields, such as cognitive science or computational linguistics. They have been used to represent the meaning of various units of natural languages, including, among others, words, phrases, and sentences. Before the deep learning tsunami, count-based vector space models had been successfully used in computational linguistics to represent the semantics of natural languages. However, the rise of neural networks in NLP popularized the use of word embeddings, which are now applied as pre-trained vectors in most machine learning architectures. This book, written by Mohammad Taher Pilehvar and Jose Camacho-Collados, provides a comprehensive and easy-to-read review of the theory and advances in vector models for NLP, focusing specially on semantic representations and their applications. It is a great introduction to different types of embeddings and the background and motivations behind them. In this sense, the authors adequately present the most relevant concepts and approaches that have been used to build vector representations. They also keep track of the most recent advances of this vibrant and fast-evolving area of research, discussing cross-lingual representations and current language models based on the Transformer. Therefore, this is a useful book for researchers interested in computational methods for semantic representations and artificial intelligence. Although some basic knowledge of machine learning may be necessary to follow a few topics, the book includes clear illustrations and explanations which make it accessible to a wide range of readers. Apart from the preface and the conclusions, the book is organized into eight chapters. In the first two, the authors introduce some of the core ideas of NLP and artificial neural networks, respectively, discussing several concepts that will be useful throughout the book. Then, Chapters 3 to 6 present different types of vector representations at the lexical level (word embeddings, graph embeddings, sense embeddings, and contextualized embeddings), followed by a brief chapter (7) about sentence and document embeddings. For each specific topic, the book includes methods and data sets to assess the quality of the embeddings. Finally, Chapter 8 raises ethical issues involved
{"title":"Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning","authors":"José Camacho-Collados, Mohammad Taher Pilehvar","doi":"10.2200/s01057ed1v01y202009hlt047","DOIUrl":"https://doi.org/10.2200/s01057ed1v01y202009hlt047","url":null,"abstract":"Word vector representations have a long tradition in several research fields, such as cognitive science or computational linguistics. They have been used to represent the meaning of various units of natural languages, including, among others, words, phrases, and sentences. Before the deep learning tsunami, count-based vector space models had been successfully used in computational linguistics to represent the semantics of natural languages. However, the rise of neural networks in NLP popularized the use of word embeddings, which are now applied as pre-trained vectors in most machine learning architectures. This book, written by Mohammad Taher Pilehvar and Jose Camacho-Collados, provides a comprehensive and easy-to-read review of the theory and advances in vector models for NLP, focusing specially on semantic representations and their applications. It is a great introduction to different types of embeddings and the background and motivations behind them. In this sense, the authors adequately present the most relevant concepts and approaches that have been used to build vector representations. They also keep track of the most recent advances of this vibrant and fast-evolving area of research, discussing cross-lingual representations and current language models based on the Transformer. Therefore, this is a useful book for researchers interested in computational methods for semantic representations and artificial intelligence. Although some basic knowledge of machine learning may be necessary to follow a few topics, the book includes clear illustrations and explanations which make it accessible to a wide range of readers. Apart from the preface and the conclusions, the book is organized into eight chapters. In the first two, the authors introduce some of the core ideas of NLP and artificial neural networks, respectively, discussing several concepts that will be useful throughout the book. Then, Chapters 3 to 6 present different types of vector representations at the lexical level (word embeddings, graph embeddings, sense embeddings, and contextualized embeddings), followed by a brief chapter (7) about sentence and document embeddings. For each specific topic, the book includes methods and data sets to assess the quality of the embeddings. Finally, Chapter 8 raises ethical issues involved","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81301721","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}
Pub Date : 2020-10-30DOI: 10.2200/s01060ed1v01y202010hlt048
M. McTear
Abstract This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years tha...
{"title":"Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots","authors":"M. McTear","doi":"10.2200/s01060ed1v01y202010hlt048","DOIUrl":"https://doi.org/10.2200/s01060ed1v01y202010hlt048","url":null,"abstract":"Abstract This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years tha...","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90142685","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}
Pub Date : 2020-04-03DOI: 10.1007/978-3-031-02174-9
Rotem Dror, Lotem Peled-Cohen, Segev Shlomov, Roi Reichart
{"title":"Statistical Significance Testing for Natural Language Processing","authors":"Rotem Dror, Lotem Peled-Cohen, Segev Shlomov, Roi Reichart","doi":"10.1007/978-3-031-02174-9","DOIUrl":"https://doi.org/10.1007/978-3-031-02174-9","url":null,"abstract":"","PeriodicalId":22125,"journal":{"name":"Synthesis Lectures on Human Language Technologies","volume":"117 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141216920","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}
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