Deep learning and linguistic representation:
"The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2021
|
| Ausgabe: | First edition |
| Schriftenreihe: | Machine learning & pattern recognition series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032623597&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | "The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge"-- |
| Umfang: | XIV, 147 Seiten Diagramme 24 cm |
| ISBN: | 9780367648749 9780367649470 |
Internformat
MARC
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| 505 | 8 | |a Introduction: Deep learning in natural language processing -- Learning syntactic structure with deep neural networks -- Machine learning and the sentence acceptability task -- Predicting human acceptability judgments in context -- Cognitively viable computational models of linguistic knowledge -- Conclusions and future work | |
| 520 | |a "The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge"-- | ||
| 650 | 4 | |a Computational linguistics | |
| 650 | 4 | |a Natural language processing (Computer science) | |
| 650 | 4 | |a Machine learning | |
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| 650 | 7 | |a Machine learning |2 fast | |
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Datensatz im Suchindex
| _version_ | 1819376572005613568 |
|---|---|
| adam_text | Contents Preface x¡ Chapter 1 ■ Introduction: Deep Learning in Natural Language Processing 1 1.1 OUTLINE OF THE BOOK 1 1.2 FROM ENGINEERING TO COGNITIVESCIENCE 4 1.3 ELEMENTS OF DEEP LEARNING 7 1.4 TYPES OF DEEP NEURAL NETWORKS 10 1.5 AN EXAMPLE APPLICATION 17 1.6 SUMMARY AND CONCLUSIONS 21 Chapter 2 ■ Learning Syntactic Structure with Deep Neural Networks 23 2.1 SUBJECT-VERB AGREEMENT 23 2.2 ARCHITECTURE AND EXPERIMENTS 24 2.3 HIERARCHICAL STRUCTURE 34 2.4 TREE DNNS 39 2.5 SUMMARY AND CONCLUSIONS 42 Chapter 3 ■ Machine Learning and the Sentence Acceptability Task 45 3.1 GRADIENCE IN SENTENCE ACCEPTABILITY 45 3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARN ING MODELS 51
Contents ■ ix viii ■ Contents 3.3 ADDING TAGS AND TREES 62 3.4 SUMMARY AND CONCLUSIONS 66 Chapter 4 ■ Predicting Human Acceptability Judgements in Context 69 4.1 ACCEPTABILITY JUDGEMENTS IN CONTEXT 69 4.2 TWO SETS OF EXPERIMENTS 75 4.3 THE COMPRESSION EFFECT AND DISCOURSE CO HERENCE 78 PREDICTING ACCEPTABILITY WITHDIFFERENT DNN MODELS 80 SUMMARY AND CONCLUSIONS 87 4.4 4.5 Chapter 5 ■ Cognitively Viable Computational Models of Lin guistic Knowledge 89 5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS? 89 MACHINE LEARNING MODELS VS FORMAL GRAM MAR 92 5.3 EXPLAINING LANGUAGE ACQUISITION 96 5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 100 5.5 SUMMARY AND CONCLUSIONS 108 5.2 Chapter 6 ■ Conclusions and Future Work 113 6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWL EDGE 113 6.2 DOMAIN-SPECIFIC LEARNING GUAGE ACQUISITION 6.3 BIASES AND DIRECTIONSFOR FUTURE WORK REFERENCES LAN 119 121 123 Author Index 139 Subject Index 145
|
| any_adam_object | 1 |
| author | Lappin, Shalom |
| author_GND | (DE-588)1077224141 |
| author_facet | Lappin, Shalom |
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| contents | Introduction: Deep learning in natural language processing -- Learning syntactic structure with deep neural networks -- Machine learning and the sentence acceptability task -- Predicting human acceptability judgments in context -- Cognitively viable computational models of linguistic knowledge -- Conclusions and future work |
| ctrlnum | (OCoLC)1256430738 (DE-599)BVBBV047218940 |
| discipline | Informatik |
| edition | First edition |
| format | Book |
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| id | DE-604.BV047218940 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:13:09Z |
| institution | BVB |
| isbn | 9780367648749 9780367649470 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032623597 |
| oclc_num | 1256430738 |
| open_access_boolean | |
| owner | DE-29 DE-739 DE-355 DE-BY-UBR |
| owner_facet | DE-29 DE-739 DE-355 DE-BY-UBR |
| physical | XIV, 147 Seiten Diagramme 24 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Machine learning & pattern recognition series |
| spellingShingle | Lappin, Shalom Deep learning and linguistic representation Introduction: Deep learning in natural language processing -- Learning syntactic structure with deep neural networks -- Machine learning and the sentence acceptability task -- Predicting human acceptability judgments in context -- Cognitively viable computational models of linguistic knowledge -- Conclusions and future work Computational linguistics Natural language processing (Computer science) Machine learning Computational linguistics fast Machine learning fast Natural language processing (Computer science) fast Maschinelles Lernen (DE-588)4193754-5 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4035843-4 |
| title | Deep learning and linguistic representation |
| title_auth | Deep learning and linguistic representation |
| title_exact_search | Deep learning and linguistic representation |
| title_full | Deep learning and linguistic representation Shalom Lappin |
| title_fullStr | Deep learning and linguistic representation Shalom Lappin |
| title_full_unstemmed | Deep learning and linguistic representation Shalom Lappin |
| title_short | Deep learning and linguistic representation |
| title_sort | deep learning and linguistic representation |
| topic | Computational linguistics Natural language processing (Computer science) Machine learning Computational linguistics fast Machine learning fast Natural language processing (Computer science) fast Maschinelles Lernen (DE-588)4193754-5 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| topic_facet | Computational linguistics Natural language processing (Computer science) Machine learning Maschinelles Lernen Computerlinguistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032623597&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lappinshalom deeplearningandlinguisticrepresentation |