| other,20-2-P01-1008,bq | We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term> . | #1798 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. |
| other,10-2-A88-1001,bq | <term> Multimedia answers </term> include <term> videodisc images </term> and heuristically-produced complete <term> sentences </term> in <term> text </term> or <term> text-to-speech form </term> . | #14892 Multimedia answers include videodisc images and heuristically-produced complete sentences intext or text-to-speech form. |
| lr,0-4-P03-1050,bq | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4489 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. |
| This paper proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent <term> corpus </term> . | #6133 This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherent corpus. | |
| tech,6-1-P84-1078,bq | This report describes <term> Paul </term> , a <term> computer text generation system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions </term> . | #13751 This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. |
| tech,25-1-H94-1084,bq | Because of the complexity of <term> documents </term> and the variety of applications which must be supported , <term> document understanding </term> requires the integration of <term> image understanding </term> with <term> text understanding </term> . | #21385 Because of the complexity of documents and the variety of applications which must be supported, document understanding requires the integration of image understanding withtext understanding. |
| lr,11-4-P04-2010,bq | Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> . | #7081 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. |
| tech,24-2-H94-1084,bq | Our <term> document understanding technology </term> is implemented in a system called <term> IDUS ( Intelligent Document Understanding System ) </term> , which creates the data for a <term> text retrieval application </term> and the <term> automatic generation of hypertext links </term> . | #21412 Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for atext retrieval application and the automatic generation of hypertext links. |
| other,29-3-P84-1078,bq | The system identities a strength of <term> antecedence recovery </term> for each of the <term> lexical substitutions </term> , and matches them against the <term> strength of potential antecedence </term> of each element in the <term> text </term> to select the proper <term> substitutions </term> for these elements . | #13816 The system identities a strength of antecedence recovery for each of the lexical substitutions, and matches them against the strength of potential antecedence of each element in thetext to select the proper substitutions for these elements. |
| other,14-4-C92-4207,bq | To reconstruct the <term> model </term> , the authors extract the <term> qualitative spatial constraints </term> from the <term> text </term> , and represent them as the <term> numerical constraints </term> on the <term> spatial attributes </term> of the <term> entities </term> . | #18468 To reconstruct the model, the authors extract the qualitative spatial constraints from thetext, and represent them as the numerical constraints on the spatial attributes of the entities. |
| other,17-1-A94-1026,bq | <term> Japanese texts </term> frequently suffer from the <term> homophone errors </term> caused by the <term> KANA-KANJI conversion </term> needed to input the <term> text </term> . | #20383 Japanese texts frequently suffer from the homophone errors caused by the KANA-KANJI conversion needed to input thetext. |
| other,12-4-P06-1013,bq | Our <term> combination methods </term> rely on <term> predominant senses </term> which are derived automatically from <term> raw text </term> . | #11022 Our combination methods rely on predominant senses which are derived automatically from raw text. |
| other,35-1-I05-4010,bq | In this paper we present our recent work on harvesting <term> English-Chinese bitexts </term> of the laws of Hong Kong from the <term> Web </term> and aligning them to the <term> subparagraph </term> level via utilizing the <term> numbering system </term> in the <term> legal text hierarchy </term> . | #8239 In this paper we present our recent work on harvesting English-Chinese bitexts of the laws of Hong Kong from the Web and aligning them to the subparagraph level via utilizing the numbering system in the legal text hierarchy. |
| tech,38-3-H01-1040,bq | We also report results of a preliminary , <term> qualitative user evaluation </term> of the <term> system </term> , which while broadly positive indicates further work needs to be done on the <term> interface </term> to make <term> users </term> aware of the increased potential of <term> IE-enhanced text browsers </term> . | #383 We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. |
| other,31-1-H01-1040,bq | In this paper we show how two standard outputs from <term> information extraction ( IE ) systems </term> - <term> named entity annotations </term> and <term> scenario templates </term> - can be used to enhance access to <term> text collections </term> via a standard <term> text browser </term> . | #305 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access totext collections via a standard text browser. |
| other,13-1-P82-1035,bq | Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other <term> edited texts </term> . | #12957 Most large text-understanding systems have been designed under the assumption that the inputtext will be in reasonably neat form, e.g., newspaper stories and other edited texts. |
| other,13-1-P84-1078,bq | This report describes <term> Paul </term> , a <term> computer text generation system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions </term> . | #13758 This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. |
| tech,24-5-P04-2010,bq | Although the system performs well within a limited textual domain , further research is needed to make it effective for <term> open-domain question answering </term> and <term> text summarisation </term> . | #7122 Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering andtext summarisation. |
| tech,17-1-H92-1095,bq | <term> Language understanding </term> work at <term> Paramax </term> focuses on applying general-purpose <term> language understanding technology </term> to <term> spoken language understanding </term> , <term> text understanding </term> , and <term> document processing </term> , integrating <term> language understanding </term> with <term> speech recognition </term> , <term> knowledge-based information retrieval </term> and <term> image understanding </term> . | #19654 Language understanding work at Paramax focuses on applying general-purpose language understanding technology to spoken language understanding,text understanding, and document processing, integrating language understanding with speech recognition, knowledge-based information retrieval and image understanding. |
| other,24-1-N03-4010,bq | The <term> JAVELIN system </term> integrates a flexible , <term> planning-based architecture </term> with a variety of <term> language processing modules </term> to provide an <term> open-domain question answering capability </term> on <term> free text </term> . | #3660 The JAVELIN system integrates a flexible, planning-based architecture with a variety of language processing modules to provide an open-domain question answering capability on free text. |
