1 |
Domain-specific coreference resolution with lexicalized features
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Toward completeness in concept extraction and classification
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Corpus-based semantic lexicon induction with web-based corroboration
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Unified model of phrasal and sentential evidence for information extraction
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Semantic class learning from the web with hyponym pattern linkage graphs
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Effective information extraction with semantic affinity patterns and relevant regions
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Exploiting role-identifying nouns and expressions for information extraction
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Learning domain-specific information extraction patterns from the web
|
|
|
|
BASE
|
|
Show details
|
|
11 |
OpinionFinder: a system for subjectivity analysis
|
|
Wilson, Theresa; Hoffmann, Paul; Somasundaran, Swapna; Kessler, Jason; Wiebe, Janyce; Choi, Yejin; Cardie, Claire; Patwardhan, Siddharth; Riloff, Ellen M.. - : Association for Computational Linguistics, 2005
|
|
BASE
|
|
Show details
|
|
12 |
Identifying sources of opinions with conditional random fields and extraction patterns
|
|
|
|
Abstract:
Journal Article ; Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a sequence tagging task, AutoSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79:3% precision and 59:5% recall using a head noun matching measure, and 81:2% precision and 60:6% recall using an overlap measure.
|
|
Keyword:
AutoSlog; Conditional random fields; Information retrieval; Opinion analysis; Opinion recognition; Sentiment classification; Sources of opinions
|
|
URL: https://collections.lib.utah.edu/ark:/87278/s6v12p0n
|
|
BASE
|
|
Hide details
|
|
13 |
Unsupervised learning of contextual role knowledge for coreference resolution
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Learning subjective nouns using extraction pattern bootstrapping
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Exploiting strong syntactic heuristics and co-training to learn semantic lexicons
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Bootstrapping method for learning semantic lexicons using extraction pattern contexts
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Inducing information extraction systems for new languages via cross-language projection
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Looking under the hood: tools for diagnosing your question answering engine
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Rule-based question answering system for reading comprehension tests
|
|
|
|
BASE
|
|
Show details
|
|
|
|