Structure discovery in natural language:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2012
|
| Schriftenreihe: | Theory and applications of natural language processing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XX, 178 S. zahlr. graph. Darst. 24 cm |
| ISBN: | 9783642259227 3642259227 |
Internformat
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Datensatz im Suchindex
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| adam_text |
IMAGE 1
CONTENTS
FOREWORD BY ANTAL VAN DEN BOSCH VII
1 INTRODUCTION 1
1.1 STRUCTURE DISCOVERY FOR LANGUAGE PROCESSING 1
1.1.1 STRUCTURE DISCOVERY PARADIGM 3
1.1.2 APPROACHES TO AUTOMATIC LANGUAGE PROCESSING 4 1.1.3
KNOWLEDGE-INTENSIVE AND KNOWLEDGE-FREE 5
1.1.4 DEGREES OF SUPERVISION 6
1.1.5 CONTRASTING STRUCTURE DISCOVERY WITH PREVIOUS APPROACHES . 7 1.2
RELATION TO GENERAL LINGUISTICS 7
1.2.1 LINGUISTIC STRUCTURALISM AND DISTRIBUTIONALISM 8 1.2.2 ADEQUACY OF
THE STRUCTURE DISCOVERY PARADIGM 9 1.3 SIMILARITY AND HOMOGENEITY IN
LANGUAGE DATA 11
1.3.1 LEVELS IN NATURAL LANGUAGE PROCESSING 11
1.3.2 SIMILARITY OF LANGUAGE UNITS 12
1.3.3 HOMOGENEITY OF SETS OF LANGUAGE UNITS 12
1.4 VISION: THE STRUCTURE DISCOVERY MACHINE 13
1.5 CONNECTING STRUCTURE DISCOVERY TO NLP TASKS 15
1.6 CONTENTS OF THIS BOOK 16
1.6.1 THEORETICAL ASPECTS OF STRUCTURE DISCOVERY 16
1.6.2 APPLICATIONS OF STRUCTURE DISCOVERY 17
1.6.3 THE FUTURE OF STRUCTURE DISCOVERY 17
2 GRAPH MODELS 19
2.1 GRAPH THEORY 19
2.1.1 NOTIONS OF GRAPH THEORY 19
2.1.2 MEASURES ON GRAPHS 23
2.2 RANDOM GRAPHS AND SMALL WORLD GRAPHS 27
2.2.1 RANDOM GRAPHS: ERDOS-RENYI MODEL 27
2.2.2 SMALL WORLD GRAPHS: WATTS-STROGATZ MODEL 28 2.2.3 PREFERENTIAL
ATTACHMENT: BARABASI-ALBERT MODEL 29
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1017147205
DIGITALISIERT DURCH
IMAGE 2
X VI CONTENTS
2.2.4 AGEING: POWER-LAWS WITH EXPONENTIAL TAILS 30
2.2.5 SEMANTIC NETWORKS: STEYVERS-TENENBAUM MODEL 32 2.2.6 CHANGING THE
POWER-LAW'S SLOPE: (A, SS) MODEL 32 2.2.7 TWO REGIMES: DOROGOVTSEV-MENDES
MODEL 35 2.2.8 FURTHER REMARKS ON SMALL WORLD GRAPH MODELS 35
2.2.9 FURTHER READING 37
3 SMALL WORLDS OF NATURAL LANGUAGE 39
3.1 POWER-LAWS IN RANK-FREQUENCY DISTRIBUTION 39
3.1.1 WORDFREQUENCY 40
3.1.2 LETTER IV-GRAMS 41
3.1.3 WORDW-GRAMS 41
3.1.4 SENTENCE FREQUENCY 44
3.1.5 OTHER POWER-LAWS IN LANGUAGE DATA 44
3.1.6 MODELLING LANGUAGE WITH POWER-LAW AWARENESS 45 3.2 SCALE-FREE
SMALL WORLDS IN LANGUAGE DATA 46
3.2.1 WORD CO-OCCURRENCE GRAPH 46
3.2.2 CO-OCCURRENCE GRAPHS OF HIGHER ORDER 51
3.2.3 SENTENCE SIMILARITY 55
3.2.4 SUMMARY ON SCALE-FREE SMALL WORLDS IN LANGUAGE DATA . . 57 3.3 AN
EMERGENT RANDOM GENERATION MODEL FOR LANGUAGE 57 3.3.1 REVIEW OF
EMERGENT RANDOM TEXT MODELS 58
3.3.2 DESIDERATA FOR RANDOM TEXT MODELS 59
3.3.3 TESTING PROPERTIES OF WORD STREAMS 60
3.3.4 WORD GENERATOR 60
3.3.5 SENTENCE GENERATOR 63
3.3.6 MEASURING AGREEMENT WITH NATURAL LANGUAGE 65 3.3.7 SUMMARY FOR THE
GENERATION MODEL 70
4 GRAPH CLUSTERING 73
4.1 REVIEW ON GRAPH CLUSTERING 73
4.1.1 INTRODUCTION TO CLUSTERING 73
4.1.2 SPECTRAL VS. NON-SPECTRAL GRAPH PARTITIONING 77 4.1.3 GRAPH
CLUSTERING ALGORITHMS 77
4.2 CHINESE WHISPERS GRAPH CLUSTERING 83
4.2.1 CHINESE WHISPERS ALGORITHM 84
4.2.2 EMPIRICAL ANALYSIS 88
4.2.3 WEIGHTING OF VERTICES 91
4.2.4 APPROXIMATING DETERMINISTIC OUTCOME 92
4.2.5 DISAMBIGUATION OF VERTICES 95
4.2.6 HIERARCHICAL DIVISIVE CHINESE WHISPERS 96
4.2.7 HIERARCHICAL AGGLOMERATIVE CHINESE WHISPERS 98 4.2.8 SUMMARY ON
CHINESE WHISPERS 99
IMAGE 3
CONTENTS XVII
5 UNSUPERVISED LANGUAGE SEPARATION 101
5.1 RELATED WORK 101
5.2 METHOD 102
5.3 EVALUATION 103
5.4 EXPERIMENTS WITH EQUISIZED PARTS FOR 10 LANGUAGES 104 5.5
EXPERIMENTS WITH BILINGUAL CORPORA 107
5.6 CASE STUDY: LANGUAGE SEPARATION FOR TWITTER 109
5.7 SUMMARY ON LANGUAGE SEPARATION ILL
6 UNSUPERVISED PART-OF-SPEECH TAGGING 113
6.1 INTRODUCTION TO UNSUPERVISED POS TAGGING 113
6.2 RELATED WORK 114
6.3 SYSTEM ARCHITECTURE 117
6.4 TAGSET 1: HIGH AND MEDIUM FREQUENCY WORDS 118
6.5 TAGSET 2: MEDIUM AND LOW FREQUENCY WORDS 122
6.6 COMBINATION OF TAGSETS 1 AND 2 124
6.7 SETTING UP THE TAGGER 125
6.7.1 LEXICON CONSTRUCTION 125
6.7.2 CONSTRUCTING THE TAGGER 126
6.7.3 MORPHOLOGICAL EXTENSION 127
6.8 DIRECT EVALUATION OF TAGGING 127
6.8.1 INFLUENCE OF SYSTEM COMPONENTS 128
6.8.2 INFLUENCE OF PARAMETERS 131
6.8.3 INFLUENCE OF CORPUS SIZE 132
6.8.4 DOMAIN SHIFTING 133
6.8.5 COMPARISON WITH CLARK [66] 134
6.9 APPLICATION-BASED EVALUATION 137
6.9.1 UNSUPERVISED POS FOR SUPERVISED POS 137
6.9.2 UNSUPERVISED POS FOR WORD SENSE DISAMBIGUATION 139 6.9.3
UNSUPERVISED POS FOR NER AND CHUNKING 141
6.10 CONCLUSION ON UNSUPERVISED POS TAGGING 144
7 WORD SENSE INDUCTION AND DISAMBIGUATION 145
7.1 RELATED WORK ON WORD SENSE INDUCTION 145
7.2 TASK-ORIENTED DEFINITION OF WSD 146
7.3 WORD SENSE INDUCTION USING GRAPH CLUSTERING 147
7.3.1 GRAPH CLUSTERING PARAMETERISATION 148
7.3.2 FEATURE ASSIGNMENT IN CONTEXT 149
7.4 EVALUATION OF WSI FEATURES IN A SUPERVISED WSD SYSTEM 149 7.4.1
MACHINE LEARNING SETUP FOR SUPERVISED WSD SYSTEM 149 7.4.2 SEMEVAL-07
LEXICAL SAMPLE TASK 151
7.4.3 LEXICAL SUBSTITUTION SYSTEM 152
7.4.4 SUBSTITUTION ACCEPTABILITY EVALUATION 153
7.5 CONCLUSION ON WORD SENSE INDUCTION AND DISAMBIGUATION 155
IMAGE 4
XVIII CONTENTS
8 CONCLUSION 157
8.1 CURRENT STATE OF STRUCTURE DISCOVERY 157
8.2 THE FUTURE OF STRUCTURE DISCOVERY 159
REFERENCES 161 |
| any_adam_object | 1 |
| author | Biemann, Chris 1977- |
| author_GND | (DE-588)13395255X |
| author_facet | Biemann, Chris 1977- |
| author_role | aut |
| author_sort | Biemann, Chris 1977- |
| author_variant | c b cb |
| building | Verbundindex |
| bvnumber | BV040598672 |
| classification_rvk | ST 306 |
| ctrlnum | (OCoLC)775738936 (DE-599)DNB1017147205 |
| dewey-full | 006.35 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.35 |
| dewey-search | 006.35 |
| dewey-sort | 16.35 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Sprachwissenschaft Mathematik |
| format | Book |
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| id | DE-604.BV040598672 |
| illustrated | Illustrated |
| indexdate | 2024-08-21T00:21:57Z |
| institution | BVB |
| isbn | 9783642259227 3642259227 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025426459 |
| oclc_num | 775738936 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM |
| owner_facet | DE-19 DE-BY-UBM |
| physical | XX, 178 S. zahlr. graph. Darst. 24 cm |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
| record_format | marc |
| series2 | Theory and applications of natural language processing |
| spelling | Biemann, Chris 1977- Verfasser (DE-588)13395255X aut Structure discovery in natural language Chris Bieman Berlin [u.a.] Springer 2012 XX, 178 S. zahlr. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Theory and applications of natural language processing Literaturangaben Mustererkennung (DE-588)4040936-3 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Wortgraph (DE-588)4585909-7 gnd rswk-swf Netzwerk Graphentheorie (DE-588)4705155-3 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Sprachdaten (DE-588)4312652-2 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Sprachverarbeitung (DE-588)4116579-2 s Sprachdaten (DE-588)4312652-2 s Mustererkennung (DE-588)4040936-3 s Wortgraph (DE-588)4585909-7 s Netzwerk Graphentheorie (DE-588)4705155-3 s Cluster-Analyse (DE-588)4070044-6 s DE-604 Künstliche Intelligenz (DE-588)4033447-8 s Computerlinguistik (DE-588)4035843-4 s 1\p DE-604 Erscheint auch als Online-Ausgabe Structure Discovery in Natural Language X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3911596&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025426459&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Biemann, Chris 1977- Structure discovery in natural language Mustererkennung (DE-588)4040936-3 gnd Computerlinguistik (DE-588)4035843-4 gnd Cluster-Analyse (DE-588)4070044-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Wortgraph (DE-588)4585909-7 gnd Netzwerk Graphentheorie (DE-588)4705155-3 gnd Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Sprachdaten (DE-588)4312652-2 gnd |
| subject_GND | (DE-588)4040936-3 (DE-588)4035843-4 (DE-588)4070044-6 (DE-588)4033447-8 (DE-588)4585909-7 (DE-588)4705155-3 (DE-588)4041354-8 (DE-588)4116579-2 (DE-588)4312652-2 |
| title | Structure discovery in natural language |
| title_auth | Structure discovery in natural language |
| title_exact_search | Structure discovery in natural language |
| title_full | Structure discovery in natural language Chris Bieman |
| title_fullStr | Structure discovery in natural language Chris Bieman |
| title_full_unstemmed | Structure discovery in natural language Chris Bieman |
| title_short | Structure discovery in natural language |
| title_sort | structure discovery in natural language |
| topic | Mustererkennung (DE-588)4040936-3 gnd Computerlinguistik (DE-588)4035843-4 gnd Cluster-Analyse (DE-588)4070044-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Wortgraph (DE-588)4585909-7 gnd Netzwerk Graphentheorie (DE-588)4705155-3 gnd Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Sprachdaten (DE-588)4312652-2 gnd |
| topic_facet | Mustererkennung Computerlinguistik Cluster-Analyse Künstliche Intelligenz Wortgraph Netzwerk Graphentheorie Natürliche Sprache Sprachverarbeitung Sprachdaten |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3911596&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025426459&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT biemannchris structurediscoveryinnaturallanguage |