| carries important information yet it is | also | time consuming to document . Given the | #11 Oral communication is ubiquitous and carries important information yet it is also time consuming to document. | ||
| part of their industry watch function . We | also | report results of a preliminary , <term> | #345 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. | ||
| database </term> . <term> Requestors </term> can | also | instruct the <term> system </term> to notify | #866 Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete. | ||
| language identification </term> . The paper | also | proposes <term> rule-reduction algorithm </term> | #1264 The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. | ||
| retrieval accuracy </term> , but much faster . We | also | provide evidence that our findings are | #1588 We also provide evidence that our findings are scalable. | ||
| to perform an exhaustive comparison , we | also | evaluate a <term> hand-crafted template-based | #2081 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. | ||
| <term> target recognition task </term> , but | also | that it is possible to get bigger performance | #3060 In this paper, we show how training data can be supplemented with text 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. | ||
| predicate-argument structures </term> . We | also | introduce a new way of automatically identifying | #3731 We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. | ||
| have in their sense coverage . Our analysis | also | highlights the importance of the issue | #4919 Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs. | ||
| </term> than that of <term> LTAG </term> . We | also | investigate the reason for that difference | #5143 We also investigate the reason for that difference. | ||
| <term> statistical translation model </term> is | also | presented that deals such <term> phrases </term> | #5619 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. | ||
| of <term> CMU 's SMT system </term> . It has | also | successfully been coupled with <term> rule-based | #6920 It has also successfully been coupled with rule-based and example based machine translation modules to build a multi engine machine translation system. | ||
| Applications of the <term> corpus </term> are | also | discussed . In this paper we present our | #7250 Applications of the corpus are also discussed. | ||
| research </term> . This piece of work has | also | laid a foundation for exploring and harvesting | #7348 This piece of work has also laid a foundation for exploring and harvesting English-Chinese bitexts in a larger volume from the Web. | ||
| equivalence </term> and <term> entailment </term> . We | also | introduce a novel <term> classification method | #7418 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. | ||
| the two <term> models </term> . The results | also | revealed an <term> upper bound </term> of <term> | #7776 The results also revealed an upper bound of accuracy of 77% with the method when using only topic information. | ||
| model ’s </term> score of 88.2 % . The article | also | introduces a new <term> algorithm </term> for | #8228 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | ||
| statisticalmachine translation system </term> . We | also | show that a good-quality <term> MT system | #8430 We also show that a good-quality MT system can be built fromscratch by starting with a very small parallel corpus (100,000 words) and exploiting a largenon-parallel corpus. | ||
| be captured by <term> chunking </term> . We | also | demonstrate how <term> semantic information | #9354 We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. | ||
| machine translation task </term> , which can | also | be viewed as a <term> stochastic tree-to-tree | #9869 Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |
