| other,12-2-H01-1042,ak | information about both the <term> human language | learning | process </term> , the <term> translation process | #595 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. | |
| other,1-4-H01-1042,ak | of <term> MT output </term> . A <term> language | learning | experiment </term> showed that <term> assessors | #630 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. | |
| tech,28-4-H01-1055,ak | can be overcome by employing <term> machine | learning | techniques </term> . In this paper , we address | #1024 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques. | |
| tech,3-2-P01-1008,ak | </term> . We present an <term> unsupervised | learning | algorithm </term> for <term> identification | #1782 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. | |
| tech,31-2-P01-1047,ak | resource sensitive logic </term> , and a <term> | learning | algorithm </term> from <term> structured data | #1978 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and alearning algorithm from structured data (based on a typing-algorithm and type-unification). | |
| tech,5-1-P01-1070,ak | describe a set of <term> supervised machine | learning | experiments </term> centering on the construction | #2132 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. | |
| tech,8-1-N03-1004,ak | <term> ensemble methods </term> in <term> machine | learning | </term> and other areas of <term> natural language | #2316 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,27-3-N03-1017,ak | relatively simple means : <term> heuristic | learning | </term> of <term> phrase translations </term> | #2617 Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. | |
| phrase translations </term> . Surprisingly , | learning | <term> phrases </term> longer than three <term> | #2633 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. | ||
| </term> longer than three <term> words </term> and | learning | <term> phrases </term> from <term> high-accuracy | #2640 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. | ||
| simple <term> unsupervised technique </term> for | learning | <term> morphology </term> by identifying <term> | #3161 We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. | ||
| tech,18-3-P03-1002,ak | ; and ( 2 ) <term> inductive decision tree | learning | </term> . The experimental results prove | #3772 It is based on: (1) an extended set of features; and (2) inductive decision tree learning. | |
| tech,8-4-P03-1033,ak | automatically derived by <term> decision tree | learning | </term> using real <term> dialogue data </term> | #4363 Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. | |
| tech,4-1-P03-1050,ak | This paper presents an <term> unsupervised | learning | approach </term> to building a <term> non-English | #4437 This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. | |
| tech,19-1-P03-1058,ak | data </term> required for <term> supervised | learning | </term> . In this paper , we evaluate an | #4821 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. | |
| tech,10-4-I05-2044,ak | dependency parser </term> based on <term> SVM | learning | </term> . The <term> left-side dependents </term> | #6686 This paper proposes a two-phase shift-reduce dependency parser based on SVM learning. | |
| tech,6-3-P05-1018,ak | coherence assessment </term> as a <term> ranking | learning | problem </term> and show that the proposed | #8634 We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. | |
| representation </term> supports the effective | learning | of a <term> ranking function </term> . Our | #8646 We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. | ||
| tech,18-3-P05-1046,ak | text </term> , general <term> unsupervised HMM | learning | </term> fails to learn useful structure in | #9091 Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains. | |
| tech,1-2-P05-2008,ak | positive or negative . Traditional <term> machine | learning | techniques </term> have been applied to this | #10420 Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. |
