| this purpose . In this paper we show how | two | standard outputs from <term> information | #280 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 standard text browser. | ||
| Korean-to-English translation system </term> consists of | two | <term> core modules </term> , <term> language | #418 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. | ||
| </term> . We reconceptualize the task into | two | distinct phases . First , a very simple | #1370 We reconceptualize the task into two distinct phases. | ||
| the form of <term> N-grams </term> ) . Over | two | distinct datasets , we find that <term> indexing | #1532 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. | ||
| template-based generation component </term> , | two | <term> rule-based sentence planners </term> | #2089 In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. | ||
| rule-based sentence planners </term> , and | two | <term> baseline sentence planners </term> . | #2095 In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. | ||
| experiments with an <term> EBMT system </term> . The | two | <term> evaluation measures </term> of the <term> | #3125 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model. | ||
| utility of this <term> constraint </term> in | two | different <term> algorithms </term> . The results | #3268 We evaluate the utility of this constraint in two different algorithms. | ||
| procedure </term> is implemented as training | two | <term> successive learners </term> . First | #3342 The bootstrapping procedure is implemented as training two successive learners. | ||
| ( SLM ) </term> . <term> FSM </term> provides | two | strategies for <term> language understanding | #3519 FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility. | ||
| <term> accuracy difference </term> between the | two | approaches is only 14.0 % , and the difference | #4886 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. | ||
| </term> working in a ' synchronous ' way . | Two | <term> hardness results </term> for the <term> | #5720 These models can be viewed as pairs of probabilistic context-free grammars working in a 'synchronous' way. Two hardness results for the class NP are reported, along with an exponential time lower-bound for certain classes of algorithms that are currently used in the literature. | ||
| parsing </term> of <term> sentences </term> with | two | or more <term> verbs </term> . Previous works | #6645 The ambiguity resolution of right-side dependencies is essential for dependency parsing of sentences with two or more verbs. | ||
| large performance difference between the | two | <term> models </term> . The results also revealed | #7771 Experimental results showed that the proposed method achieves almost 60% accuracy and that there is not a large performance difference between the two models. | ||
| </term> to our prior work . We evaluate across | two | <term> corpora </term> ( conversational telephone | #9513 We evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. | ||
| results </term> . In this paper , we first train | two | <term> statistical word alignment models </term> | #9724 In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. | ||
| respectively , and then interpolate these | two | <term> models </term> to improve the <term> domain-specific | #9745 In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. | ||
| </term> has been investigated systematically on | two | different <term> language pairs </term> . The | #11356 The correlation of the new measure with human judgment has been investigated systematically on two different language pairs. | ||
| dialogue </term> . We extend prior work in | two | ways . We first apply approaches that have | #11422 We extend prior work in two ways. | ||
| predicting <term> subtopic boundaries </term> are | two | distinct tasks : ( 1 ) for predicting <term> | #11478 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. |
