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Domain-Specific Term-List Expansion Using Existing Linguistic Resources
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In: DTIC (2002)
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62 |
Improved Word-Level Alignment: Injecting Knowledge about MT Divergences
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In: DTIC (2002)
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63 |
Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation
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In: DTIC (2002)
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64 |
Creating Parsing Lexicons from Semantic Lexicons Automatically and Its Applications
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In: DTIC (2002)
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65 |
Constraints on the Generation of Tense, Aspect, and Connecting Words from Temporal Expressions
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In: DTIC (2002)
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66 |
Construction of a Chinese-English Verb Lexicon for Embedded Machine Translation in Cross-Language Information Retrieval
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67 |
Domain-Specific Term-List Expansion Using Existing Linguistic Resources
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68 |
Constraints on the Generation of Tense, Aspect, and Connecting Words from Temporal Expressions
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69 |
Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation
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70 |
Handling Translation Divergences in Generation-Heavy Hybrid Machine Translation
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72 |
Efficient Language Independent Generation from Lexical Conceptual Structure
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In: DTIC (2001)
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73 |
Building a Chinese-English Mapping Between Verb Concepts for Multilingual Applications
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In: DTIC (2001)
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74 |
Large Scale Language Independent Generation Using Thematic Hierarchies
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In: DTIC (2001)
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75 |
Mapping Lexical Entries in a Verbs Database to WordNet Senses
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In: DTIC (2001)
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76 |
Spanish Language Processing at University of Maryland: Building Infrastructure for Multilingual Applications
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In: DTIC (2001)
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77 |
Mapping WorldNet Senses to a Lexical Database of Verbs
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In: DTIC (2001)
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78 |
Efficient Language Independent Generation from Lexical Conceptual Structures
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79 |
Lexical Resource Integration across the Syntax-Semantics Interface
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80 |
Mapping Lexical Entries in a Verbs Database to WordNet Senses
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Abstract:
This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet senses. The verbs were initially grouped into 491 classes based on syntactic categories. Mapping these classified verbs into WordNet senses provides a resource that may be used for disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 disambiguated entries, representing 1442 verb entries from 167 of the categories; (2) word sense probabilities based on frequency counts in a previously tagged corpus; (3) semantic similarity of WordNet senses for verbs within the same class; (4) probabilistic correlations between WordNet data and attributes of the verb classes. The best results achieved 72% precision and 58% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring WordNet sense, and an upper bound of 87% precision and 75% recall for human judgment. (Cross-referenced as UMIACS-TR-2001-18) (Cross-referenced as LAMP-TR-068)
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URL: http://hdl.handle.net/1903/1126
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