| other,31-1-H01-1040,bq | - can be used to enhance access to <term> | text | collections </term> via a standard <term> text | #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,11-7-H01-1042,bq | six extracts of <term> translated newswire | text | </term> . Some of the extracts were <term> | #695 Subjects were given a set of up to six extracts of translated newswire text. |
| other,20-2-P01-1008,bq | translations </term> of the same <term> source | text | </term> . Our approach yields <term> phrasal | #1798 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. |
| other,31-1-N03-1018,bq | progressing from generation of <term> true | text | </term> through its transformation into the | #2699 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. |
| other,13-2-N03-2003,bq | data </term> can be supplemented with <term> | text | </term> from the <term> web </term> filtered | #3041 In this paper, we show how training data can be supplemented withtext from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
| other,24-1-N03-4010,bq | answering capability </term> on <term> free | text | </term> . The demonstration will focus on | #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. |
| lr,1-3-P03-1050,bq | training resources </term> . No <term> parallel | text | </term> is needed after the <term> training | #4478 No parallel text is needed after the training phase. |
| tech,3-1-C04-1116,bq | smaller and more robust . We present a <term> | text | mining method </term> for finding <term> synonymous | #6095 We present atext mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. |
| tech,26-3-P04-2005,bq | Sense Disambiguation ( WSD ) </term> and <term> | Text | Summarisation </term> . Our method takes | #6955 Topic signatures can be useful in a number of Natural Language Processing (NLP) applications, such as Word Sense Disambiguation (WSD) andText Summarisation. |
| lr,11-4-P04-2010,bq | <term> pronouns </term> in <term> unannotated | text | </term> by using a fully automatic sequence | #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. |
| other,24-4-I05-2014,bq | systems </term> outputting <term> unsegmented | texts | </term> with , for instance , <term> statistical | #7771 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
| other,35-1-I05-4010,bq | numbering system </term> in the <term> legal | text | hierarchy </term> . Basic methodology and | #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,15-2-N06-4001,bq | researchers who are not experts in <term> | text | mining </term> . As evidence of its usefulness | #10891 InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts intext mining. |
| other,12-4-P06-1013,bq | are derived automatically from <term> raw | text | </term> . Experiments using the <term> SemCor | #11022 Our combination methods rely on predominant senses which are derived automatically from raw text. |
| papers in English , many systems to run off | texts | have been developed . In this paper , we | #12231 In order to meet the needs of a publication of papers in English, many systems to run off texts have been developed. | |
| other,13-1-P82-1035,bq | under the assumption that the input <term> | text | </term> will be in reasonably neat form , | #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. |
| tech,6-1-P84-1078,bq | describes <term> Paul </term> , a <term> computer | text | generation system </term> designed to create | #13751 This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. |
| other,28-1-C86-1132,bq | sublanguages </term> with <term> stereotyped | text | structure </term> . <term> RAREAS </term> draws | #13943 This paper describes a system (RAREAS) which synthesizes marine weather forecasts directly from formatted weather data. Such synthesis appears feasible in certain natural sublanguages with stereotyped text structure. |
| other,10-2-A88-1001,bq | 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. |
| other,6-2-C88-1044,bq | </term> . We examine a broad range of <term> | texts | </term> to show how the distribution of <term> | #15199 We examine a broad range oftexts to show how the distribution of demonstrative forms and functions is genre dependent. |
