| </term> from <term> structured data </term> ( | based | on a <term> typing-algorithm </term> and <term> | #1983 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data ( based on a typing-algorithm and type-unification). | |
| approach to question answering </term> which is | based | on combining the results from different | #2336 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. | |
| implementation of the <term> model </term> | based | on <term> finite-state models </term> , demonstrate | #2751 We present an implementation of the modelbased on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text. | |
| translation quality </term> of <term> EBMT </term> | based | on a small-sized <term> bilingual corpus </term> | #3088 In order to boost the translation quality of EBMTbased on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. | |
| on <term> block selection criteria </term> | based | on <term> unigram </term> counts and <term> phrase | #3469 We show experimental results on block selection criteriabased on unigram counts and phrase length. | |
| <term> dialogue system </term> . We build this | based | on both <term> Finite State Model ( FSM ) | #3499 We build this based on both Finite State Model (FSM) and Statistical Learning Model (SLM). | |
| central to our <term> IE paradigm </term> . It is | based | on : ( 1 ) an extended set of <term> features | #3752 It is based on: (1) an extended set of features; and (2) inductive decision tree learning. | |
| tech,3-1-P03-1022,bq | data </term> . We apply a <term> decision tree | based | approach </term> to <term> pronoun resolution | #3978 We apply a decision tree based approach to pronoun resolution in spoken dialogue. |
| to understand <term> user utterances </term> | based | on the <term> context </term> of a <term> dialogue | #4147 This process enables the system to understand user utterancesbased on the context of a dialogue. | |
| for resolving this <term> ambiguity </term> | based | on <term> statistical information </term> obtained | #4225 This paper proposes a method for resolving this ambiguitybased on statistical information obtained from dialogue corpora. | |
| dimensions . <term> Dialogue strategies </term> | based | on the <term> user modeling </term> are implemented | #4382 Dialogue strategiesbased on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. | |
| </term> . The <term> stemming model </term> is | based | on <term> statistical machine translation | #4450 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. | |
| built a <term> generation algorithm </term> | based | on the results . The evaluation using another | #5706 We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithmbased on the results. | |
| tech,3-5-C04-1112,bq | model </term> . Also , the <term> WSD system | based | on lemmas </term> is smaller and more robust | #6083 Also, the WSD system based on lemmas is smaller and more robust. |
| finding <term> synonymous expressions </term> | based | on the <term> distributional hypothesis </term> | #6102 We present a text mining method for finding synonymous expressionsbased on the distributional hypothesis in a set of coherent corpora. | |
| coherent <term> corpus </term> . Our approach is | based | on the idea that one person tends to use | #6142 Our approach is based on the idea that one person tends to use one expression for one meaning. | |
| We show that various <term> features </term> | based | on the structure of <term> email-threads </term> | #6287 We show that various featuresbased on the structure of email-threads can be used to improve upon lexical similarity of discourse segments for question-answer pairing. | |
| for <term> word sense disambiguation </term> | based | on <term> parallel corpora </term> . The method | #6445 The paper presents a method for word sense disambiguationbased on parallel corpora. | |
| alignment </term> and <term> word clustering </term> | based | on <term> automatic extraction of translation | #6461 The method exploits recent advances in word alignment and word clusteringbased on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. | |
| <term> features </term> of <term> sentences </term> | based | on <term> semantic similarity measures </term> | #6696 This system identifies features of sentencesbased on semantic similarity measures and discourse structure. |
