| other,31-1-H01-1040,ak | - 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. | |
| tech,36-1-H01-1040,ak | text collections </term> via a standard <term> | text | browser </term> . We describe how this information | #310 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 to text collections via a standardtext browser. | |
| tech,38-3-H01-1040,ak | increased potential of <term> IE-enhanced | text | browsers </term> . At MIT Lincoln Laboratory | #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. | |
| up to six extracts of translated newswire | text | . Some of the extracts were <term> expert | #695 Subjects were given a set of up to six extracts of translated newswire text. | ||
| other,20-2-P01-1008,ak | translations </term> of the same <term> source | text | </term> . Our approach yields <term> phrasal | #1799 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. | |
| tech,29-1-N03-1018,ak | progressing from <term> generation of true | text | </term> through its transformation into the | #2700 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,38-3-N03-1018,ak | translation lexicons </term> from printed <term> | text | </term> . We present an application of <term> | #2783 We present an implementation of the model based 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 printedtext. | |
| other,13-2-N03-2003,ak | data </term> can be supplemented with <term> | text | </term> from the <term> web </term> filtered | #3042 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,ak | answering capability </term> on <term> free | text | </term> . The demonstration will focus on | #3661 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,19-2-N03-4010,ak | answer candidates </term> from the given <term> | text | corpus </term> . The operation of the <term> | #3682 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the giventext corpus. | |
| lr,1-3-P03-1050,ak | training resources </term> . No <term> parallel | text | </term> is needed after the <term> training | #4480 No parallel text is needed after the training phase. | |
| lr,0-4-P03-1050,ak | phase </term> . <term> Monolingual , unannotated | text | </term> can be used to further improve the | #4491 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. | |
| lr,26-6-P03-1050,ak | affix lists </term> , and <term> human annotated | text | </term> , in addition to an <term> unsupervised | #4562 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. | |
| other,16-7-P03-1050,ak | average precision </term> over <term> unstemmed | text | </term> , and 96 % of the performance of | #4588 Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above. | |
| tech,7-1-H05-1032,ak | presents a <term> Bayesian model </term> for <term> | text | summarization </term> , which explicitly | #5370 The paper presents a Bayesian model fortext summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. | |
| other,24-1-H05-1032,ak | judgments </term> are distributed over the <term> | text | </term> . Comparison is made against <term> | #5387 The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over thetext. | |
| other,12-2-H05-1032,ak | <term> test data </term> from <term> Japanese news | texts | </term> . It is found that the <term> Bayesian | #5403 Comparison is made against non Bayesian summarizers, using test data from Japanese news texts. | |
| other,13-1-I05-2013,ak | which takes as <term> input </term> a <term> raw | text | </term> in <term> French </term> and produces | #6093 We present a tool, called ILIMP, which takes as input a raw text in French and produces as output the same text in which every occurrence of the pronoun il is tagged either with tag [ANA] for anaphoric or [IMP] for impersonal or expletive. | |
| other,23-1-I05-2013,ak | produces as <term> output </term> the same <term> | text | </term> in which every <term> occurrence </term> | #6102 We present a tool, called ILIMP, which takes as input a raw text in French and produces as output the sametext in which every occurrence of the pronoun il is tagged either with tag [ANA] for anaphoric or [IMP] for impersonal or expletive. | |
| commercial systems outputting unsegmented | texts | with , for instance , <term> statistical | #6314 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. |
