| tech,4-2-H01-1058,ak | . We find that simple <term> interpolation | methods | </term> , like <term> log-linear and linear | #1049 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. | |
| performance </term> further . We suggest a | method | that mimics the behavior of the <term> oracle | #1155 We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. | ||
| </term> or a <term> decision tree </term> . The | method | amounts to tagging <term> LMs </term> with <term> | #1173 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence. | ||
| tech,6-2-P01-1004,ak | segment order-sensitive string comparison | methods | </term> , and run each over both character | #1500 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range of local segment contiguity models (in the form of N-grams). | |
| tech,7-4-P01-1004,ak | optimum configuration , <term> bag-of-words | methods | </term> are shown to be equivalent to <term> | #1567 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. | |
| tech,15-4-P01-1004,ak | equivalent to <term> segment order-sensitive | methods | </term> in terms of <term> retrieval accuracy | #1576 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. | |
| results of a practical evaluation of this | method | on a <term> wide coverage English grammar | #1744 The results of a practical evaluation of this method on a wide coverage English grammar are given. | ||
| tech,16-1-P01-1008,ak | systems use <term> manual or semi-automatic | methods | </term> to collect <term> paraphrases </term> | #1773 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. | |
| target variables . This paper describes a | method | for <term> utterance classification </term> | #2210 This paper describes a method for utterance classification that does not require manual transcription of training data. | ||
| </term> of <term> training data </term> . The | method | combines <term> domain independent acoustic | #2225 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. | ||
| <term> manual transcription </term> . In our | method | , <term> unsupervised training </term> is first | #2258 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. | ||
| <term> classification accuracy </term> of the | method | is evaluated on three different <term> spoken | #2296 The classification accuracy of the method is evaluated on three different spoken language system domains. | ||
| tech,5-1-N03-1004,ak | Motivated by the success of <term> ensemble | methods | </term> in <term> machine learning </term> and | #2313 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. | |
| tech,8-3-N03-1026,ak | the use of standard <term> parser evaluation | methods | </term> for automatically evaluating the <term> | #2849 Furthermore, we propose the use of standard parser evaluation methods for automatically evaluating the summarization quality of sentence condensation systems. | |
| <term> HDAGs </term> . We applied the proposed | method | to <term> question classification </term> and | #3844 We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function. | ||
| tech,17-4-P03-1005,ak | kernel functions </term> and <term> baseline | methods | </term> . Previous research has demonstrated | #3883 The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods. | |
| tech,16-2-P03-1009,ak | Information Bottleneck and nearest neighbour | methods | </term> . In contrast to previous work , | #3924 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. | |
| </term> can be improved . This paper proposes a | method | for resolving this <term> ambiguity </term> | #4222 This paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora. | ||
| dialogue corpora </term> . Unlike conventional | methods | that use <term> hand-crafted rules </term> | #4238 Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process. | ||
| <term> hand-crafted rules </term> , the proposed | method | enables easy design of the <term> discourse | #4246 Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process. |
