Preface |
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ix | |
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Natural language processing |
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1 | (22) |
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2 | (3) |
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5 | (4) |
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5 | (1) |
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6 | (1) |
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7 | (1) |
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8 | (1) |
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9 | (8) |
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Sentence delimiters and tokenizers |
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9 | (2) |
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11 | (2) |
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Noun phrase and name recognizers |
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13 | (2) |
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15 | (2) |
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17 | (6) |
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23 | (52) |
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26 | (1) |
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27 | (2) |
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29 | (15) |
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29 | (3) |
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32 | (4) |
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36 | (6) |
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42 | (2) |
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Evaluating search engines |
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44 | (5) |
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44 | (1) |
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45 | (2) |
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47 | (1) |
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48 | (1) |
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Attempts to enhance search performance |
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49 | (7) |
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Query expansion and thesauri |
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50 | (2) |
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Query expansion from relevance information* |
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52 | (4) |
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The future of Web searching |
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56 | (8) |
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57 | (2) |
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59 | (3) |
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Ranking and reranking documents |
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62 | (1) |
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The state of online search |
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63 | (1) |
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Summary of information retrieval |
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64 | (11) |
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75 | (44) |
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The Message Understanding Conferences |
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76 | (2) |
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78 | (3) |
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Finite automata in FASTUS |
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81 | (12) |
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Finite State Machines and regular languages |
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81 | (2) |
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Finite State Machines as parsers |
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83 | (10) |
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Pushdown automata and context-free grammars |
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93 | (13) |
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93 | (2) |
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95 | (2) |
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Parsing with a pushdown automaton |
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97 | (4) |
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Coping with incompleteness and ambiguity |
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101 | (5) |
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Limitations of current technology and future research |
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106 | (5) |
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Explicit versus implicit statements |
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107 | (2) |
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Machine learning for information extraction |
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109 | (1) |
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Statistical language models for information extraction |
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110 | (1) |
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Summary of information extraction |
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111 | (8) |
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119 | (54) |
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Overview of categorization tasks and methods |
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120 | (5) |
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Handcrafted rule based methods |
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125 | (2) |
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Inductive learning for text classification |
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127 | (21) |
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129 | (5) |
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134 | (7) |
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Decision trees and decision lists |
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141 | (7) |
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Nearest Neighbor algorithms |
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148 | (2) |
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150 | (5) |
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150 | (1) |
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151 | (2) |
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Using multiple classifiers |
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153 | (2) |
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Evaluation of text categorization systems |
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155 | (18) |
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155 | (2) |
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157 | (5) |
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162 | (1) |
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163 | (10) |
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173 | (46) |
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174 | (4) |
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Reference and coreference |
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178 | (13) |
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180 | (6) |
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186 | (5) |
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191 | (17) |
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192 | (3) |
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Constructing summaries from document fragments |
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195 | (7) |
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Multi-document summarization (MDS) |
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202 | (6) |
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Testing of automatic summarization programs |
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208 | (3) |
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Evaluation problems in summarization research |
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208 | (1) |
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Building a corpus for training and testing |
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209 | (2) |
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Prospects for text mining and NLP |
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211 | (8) |
Index |
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219 | |