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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Lexical competition in native and nonnative auditory word recognition Lancaster, Alia. - 2018 BASE Show details 2 ARCHITECTURE, MODELS, AND ALGORITHMS FOR TEXTUAL SIMILARITY He, Hua. - 2018 BASE Show details 3 SES-RELATED DIFFERENCES IN WORD LEARNING: EFFECTS OF COGNITIVE INHIBITION AND WORD LEARNING Hollister, Erin Marie. - 2016 BASE Show details 4 Fast mapping in linguistic context: Processing and complexity effects Arnold, Alison Reese. - 2015 BASE Show details 5 Interactions between language experience and cognitive abilities in word learning and word recognition Morini, Giovanna. - 2014 BASE Show details 6 PRESCHOOLERS' WORD LEARNING DURING SHARED STORYBOOK READING INTERACTIONS AND CLINICAL IMPLICATIONS FOR EARLY INTERVENTION O'Fallon, Maura Kathleen. - 2014 BASE Show details 7 Adult Readers' Calibration of Word Learning Parkinson, Meghan. - 2011 BASE Show details 8 Infants' Ability to Learn New Words Across Accent Panza, Sabrina. - 2011 BASE Show details 9 Infant speech perception in noise and vocabulary outcomes Singer, Emily R.. - 2008 BASE Show details 10 Combining Linguistic and Machine Learning Techniques for Word Alignment Improvement Ayan, Necip Fazil. - 2005 Abstract: Alignment of words, i.e., detection of corresponding units between two sentences that are translations of each other, has been shown to be crucial for the success of many NLP applications such as statistical machine translation (MT), construction of bilingual lexicons, word-sense disambiguation, and projection of resources between languages. With the availability of large parallel texts, statistical word alignment systems have proven to be quite successful on many language pairs. However, these systems are still faced with several challenges due to the complexity of the word alignment problem, lack of enough training data, difficulty learning statistics correctly, translation divergences, and lack of a means for incremental incorporation of linguistic knowledge. This thesis presents two new frameworks to improve existing word alignments using supervised learning techniques. In the first framework, two rule-based approaches are introduced. The first approach, Divergence Unraveling for Statistical MT (DUSTer), specifically targets translation divergences and corrects the alignment links related to them using a set of manually-crafted, linguistically-motivated rules. In the second approach, Alignment Link Projection (ALP), the rules are generated automatically by adapting transformation-based error-driven learning to the word alignment problem. By conditioning the rules on initial alignment and linguistic properties of the words, ALP manages to categorize the errors of the initial system and correct them. The second framework, Multi-Align, is an alignment combination framework based on classifier ensembles. The thesis presents a neural-network based implementation of Multi-Align, called NeurAlign. By treating individual alignments as classifiers, NeurAlign builds an additional model to learn how to combine the input alignments effectively. The evaluations show that the proposed techniques yield significant improvements (up to 40% relative error reduction) over existing word alignment systems on four different language pairs, even with limited manually annotated data. Moreover, all three systems allow an easy integration of linguistic knowledge into statistical models without the need for large modifications to existing systems. Finally, the improvements are analyzed using various measures, including the impact of improved word alignments in an external application---phrase-based MT. Keyword: Classifier Ensemble; Computational Linguistics; Computer Science; Machine Learning; Machine Translation; Natural Language Processing; Word Alignment URL: http://hdl.handle.net/1903/3126 BASE Hide details

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