<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .
We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare several , previously proposed <term> phrase-based translation models </term> .
Overall <term> summarization </term> quality of the proposed <term> system </term> is state-of-the-art , with guaranteed <term> grammaticality </term> of the <term> system output </term> due to the use of a <term> constraint-based parser/generator </term> .
We applied the proposed method to <term> question classification </term> and <term> sentence alignment tasks </term> to evaluate its performance as a <term> similarity measure </term> and a <term> kernel function </term> .
A novel <term> evaluation scheme </term> is proposed which accounts for the effect of <term> polysemy </term> on the <term> clusters </term> , offering us a good insight into the potential and limitations of <term> semantically classifying </term><term> undisambiguated SCF data </term> .
Unlike conventional methods that use <term> hand-crafted rules </term> , the proposed <term> method </term> enables easy design of the <term> discourse understanding process </term> .
Experiment results have shown that a <term> system </term> that exploits the proposed <term> method </term> performs sufficiently and that holding multiple <term> candidates </term> for <term> understanding </term> results is effective .
The evaluation using another 23 subjects showed that the proposed method could effectively generate proper <term> referring expressions </term> .
Under this framework , a <term> joint source-channel transliteration model </term> , also called <term> n-gram transliteration model ( ngram TM ) </term> , is further proposed to model the <term> transliteration process </term> .
We evaluate the proposed methods through several <term> transliteration/back transliteration </term> experiments for <term> English/Chinese and English/Japanese language pairs </term> .
Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the <term> transliteration accuracy </term> significantly .
Our proposed method improves the <term> accuracy </term> of our <term> term aggregation system </term> , showing that our approach is successful .
A <term> method </term> for producing such <term> phrases </term> from a <term> word-aligned corpora </term> is proposed .
Experimental results are presented , that demonstrate how the proposed <term> method </term> allows to better generalize from the <term> training data </term> .
We first apply approaches that have been proposed for <term> predicting top-level topic shifts </term> to the problem of <term> identifying subtopic boundaries </term> .
as a device to represent and to use different <term> dialog schemata </term> proposed in empirical <term> conversation analysis </term> ;
An <term> entity-oriented approach to restricted-domain parsing </term> is proposed .
A new approach for <term> Interactive Machine Translation </term> where the <term> author </term> interacts during the creation or the modification of the <term> document </term> is proposed .
We show that the proposed approach is more describable than other approaches such as those employing a traditional <term> generative phonological approach </term> .
In this paper <term> discourse segments </term> are defined and a method for <term> discourse segmentation </term> primarily based on <term> abduction </term> of <term> temporal relations </term> between <term> segments </term> is proposed .
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