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检索条件"机构=Human Language Technology Center Department of Electronic and Computer Engineering"

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KELVIN: Extracting knowledge from large text collections

KELVIN: Extracting knowledge from large text collections

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2014 AAAI Fall Symposium

作者： Mayfield, James McNamee, Paul Harman, Craig Finin, Tim Lawrie, Dawn Johns Hopkins University Human Language Technology Center of Excellence BaltimoreMD United States Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Computer Science Department Loyola University Maryland BaltimoreMD United States

ISBN: (纸本)9781577356967

We describe the KELVIN system for extracting entities and relations from large text collections and its usc in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and technology. The Cold Start task starts with an empty knowledge based defined by an ontology of entity types. properties and relations. Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to dc-emphasize the use of entities that appear in a background knowledge base such as Wikipedia. Interesting features of KELVIN include a cross-document ent ity coreference module based on entity mentions, removal of suspect intra-document conference chains, a slot value cons olidator for entities, the application of inference rules to exp and the number of asserted facts and a set of analysis and browsing tools supporting development. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Knowledge based systems

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***: A platform for public health surveillance using Twitter 28

***: A platform for public health surveillance using Twitter

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28th AAAI Conference on Artificial Intelligence, AAAI 2014

作者： Dredze, Mark Cheng, Renyuan Paul, Michael J. Broniatowski, David Human Language Technology Center of Excellenc Johns Hopkins University BaltimoreMD21211 United States Department of Computer Science Johns Hopkins University BaltimoreMD21211 United States Department of Engineering Management and Systems Engineering George Washington University WashingtonDC United States

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Emotion analysis of children's stories with context information

Emotion analysis of children's stories with context informat...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Zhengchen Zhang Minghui Dong Shuzhi Sam Ge Human Language Technology Department Institute for Infocomm Research (I2R) A *STAR Singapore Robotics Institute University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering and Social Robotics Laboratory National University of Singapore Singapore

ISBN: (纸本)9781479942503

In this paper, we analyse the emotion of children's stories in sentence level by considering the context information. We demonstrate that the emotion of a sentence is not only dependent on its content, but also affected by its neighbours in a story. A Hidden Markov Model (HMM) based method is proposed to model the emotion sequence and to detect whether a sentence is neutral or not. We show the important features for emotion detection by studying a children's story corpus. An empirical evaluation is conducted to investigate the efficiency of the model. The results demonstrate that the proposed method can achieve competitive performance with the state-of-the-art methods, and it is affected more slightly by the training set than traditional classification methods. Classifier fusion is applied to combine different methods to achieve better results.

关键词： Hidden Markov models Training Accuracy Support vector machines Testing Feature extraction Context

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Bayesian Induction of Bracketing Inversion Transduction Grammars 6

Bayesian Induction of Bracketing Inversion Transduction Gram...

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6th International Joint Conference on Natural language Processing, IJCNLP 2013

作者： Saers, Markus Wu, Dekai Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9784990734800

We present a novel approach to learning phrasal inversion transduction grammars via Bayesian MAP (maximum a posteriori) or information-theoretic MDL (minimum description length) model optimization so as to incorporate simultaneously the choices of model structure as well as parameters. In comparison to most current SMT approaches, the model learns phrase translation lexicons that (a) do not require enormous amounts of run-time memory, (b) contain significantly less redundancy, and (c) provide an obvious basis for generalization to abstract translation schemas. Model structure choice is biased by a description length prior, while parameter choice is driven by data likelihood biased by a parameter prior. The search over possible model structures is made feasible by a novel top-down rule segmenting heuristic which efficiently incorporates estimates of the posterior probabilities. Since the priors reward model parsimony, the learned grammar is very concise and still performs significantly better than the maximum likelihood driven bottom-up rule chunking baseline. © IJCNLP *** right reserved.

关键词： Bacteriophages

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Improving machine translation by training against an automatic semantic frame based evaluation metric

Improving machine translation by training against an automat...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Lo, Chi-Kiu Addanki, Karteek Saers, Markus Wu, Dekai HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781937284510

We present the first ever results showing that tuning a machine translation system against a semantic frame based objective function, MEANT, produces more robustly adequate translations than tuning against BLEU or TER as measured across commonly used metrics and human subjective evaluation. Moreover, for informal web forum data, human evaluators preferred MEANT-tuned systems over BLEU-or TER-tuned systems by a significantly wider margin than that for formal newswire - even though automatic semantic parsing might be expected to fare worse on informal language. We argue that by preserving the meaning of the translations as captured by semantic frames right in the training process, an MT system is constrained to make more accurate choices of both lexical and reordering rules. As a result, MT systems tuned against semantic frame based MT evaluation metrics produce output that is more adequate. Tuning a machine translation system against a semantic frame based objective function is independent of the translation model paradigm, so, any translation model can benefit from the semantic knowledge incorporated to improve translation adequacy through our approach. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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MEANT at WMT 2013: A tunable, accurate yet inexpensive semantic frame based MT evaluation metric 8

MEANT at WMT 2013: A tunable, accurate yet inexpensive seman...

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8th Workshop on Statistical Machine Translation, WMT 2013

作者： Lo, Chi-Kiu Wu, Dekai HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781937284572

The linguistically transparent MEANT and UMEANT metrics are tunable, simple yet highly effective, fully automatic approximation to the human HMEANT MT evaluation metric which measures semantic frame similarity between MT output and reference translations. In this paper, we describe HKUST's submission to the WMT 2013 metrics evaluation task, MEANT and UMEANT. MEANT is optimized by tuning a small number of weights-one for each semantic role label-so as to maximize correlation with human adequacy judgment on a development set. UMEANT is an unsupervised version where weights for each semantic role label are estimated via an inexpensive unsupervised approach, as opposed to MEANT's supervised method relying on more expensive grid search. In this paper, we present a battery of experiments for optimizing MEANT on different development sets to determine the set of weights that maximize MEANT's accuracy and stability. Evaluated on test sets from the WMT 2012/2011 metrics evaluation, both MEANT and UMEANT achieve competitive correlations with human judgments using nothing more than a monolingual corpus and an automatic shallow semantic parser. © 2013 Association for Computational Linguistics

关键词： Semantics

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MULTIMODAL MUSIC EMOTION CLASSIFICATION USING ADABOOST WITH DECISION STUMPS

MULTIMODAL MUSIC EMOTION CLASSIFICATION USING ADABOOST WITH ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Dan Su Pascale Fung Nicolas Auguin Human Language Technology Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9781479903573

We propose using AdaBoost with decision stumps to implement multimodal music emotion classification (MEC) as a more appropriate alternative to the conventional SVMs. By modeling the presence or absence of salient phrases in the lyric texts and seeking for proper thresholds for certain audio signal features, it exploits interdependencies between aspects from both modalities in the multimodal MEC system to make the final classification. It can especially prevent the "short text problem" in lyrics. Our accuracy reached an average of 78.19% for classifying 3766 unique songs into 14 emotion categories, with a statistically significant improvement over the audio-only and lyrics-only monomodal MEC systems. We also show that the proposed AdaBoost with decision stumps method performs statistically better on multimodal MEC than the well-known SVM classifier, which only has an average accuracy of 72.08%.

关键词： AdaBoost Decision Stumps Music Emotion Multimodal ADABOOST Emotions Music most economic control CCL28 gene Taxonomy audio signals Multiple signal classification

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Improved mixed language speech recognition using asymmetric acoustic model and language model with code-switch inversion constraints

Improved mixed language speech recognition using asymmetric ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ying Li Pascale Fung Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479903573

We propose an integrated framework for large vocabulary continuous mixed language speech recognition that handles the accent effect in the bilingual acoustic model and the inversion constraint well known to linguists in the language model. Our asymmetric acoustic model with phone set extension improves upon previous work by striking a balance between data and phonetic knowledge. Our language model improves upon previous work by (1) using the inversion constraint to predict code switching points in the mixed language and (2) integrating a code-switch prediction model, a translation model and a reconstruction model together. This integration means that our language model avoids the pitfall of propagated error that could arise from decoupling these steps. Finally, a WFST-based decoder integrates the acoustic models, code-switch language model and a monolingual language model in the matrix language all together. Our system reduces word error rate by 1.88% on a lecture speech corpus and by 2.43% on a lunch conversation corpus, with statistical significance, over the conventional bilingual acoustic model and interpolated language model.

关键词： Speech Acoustics Adaptation models Predictive models Speech recognition Data models Hidden Markov models

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Detecting and Correcting Transcription Discrepancies between Thai Parliament Meeting Speech Utterances and their Official Meeting Reports

Detecting and Correcting Transcription Discrepancies between...

引用

作者： Natnarong. Puangsri Atiwong. Suchato Proadpran. Punyabukkana Chai. Wutiwiwatchai Department of Computer Engineering Faculty of Engineering Chulalongkorn University Human Language Technology Laboratory National Electronics and Computer Technology Center

Speech data and their accurate transcriptions are essential to acoustic models training of modern Automatic Speech Recognition (ASR) systems including ones applied to the parliament meeting speech transcription system. In such a task, the amount of speech data is in abundance but there are discrepancies between what were actually uttered, or word-for-word transcription, and their corresponding texts in the official meeting reports. This work proposes a method for automatically detecting locations of such discrepancies. Rules derived from a handbook for Thai parliament stenographer and patterns of discrepancies were used to generate alternative hypotheses supplied, with texts obtained directly from the reports, to a forced-alignment procedure. The forced-alignment procedure selects the best hypothesis for each speech utterance. Experimental results show that 72.6% of syllabic discrepancies were correctly detected while interference to correctly transcribed syllables was kept minimal. Moreover, the proposed method shows a premise in increasing accuracy of word-for-word transcription and achieves 96.5% of accuracy.

关键词： Automatic Speech Recognition, Automatic Transcription System, Forced Alignment, Parliamentary Speech

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Thai phonetization of English words using English syllables

Thai phonetization of English words using English syllables

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International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Kitanan Pitakpawatkul Atiwong Suchato Proadpran Punyabukkana Chai Wutiwiwatchai Spoken Language Systems Research Group Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Human Language Technology Laboratory National Electronics and Computer Technology Center Thailand

In Thai, it is not easy to find transcriptions of Roman characters such as English words. This leads to problems in Thai text-to-speech when facing with English words among Thai writing. Automatic English to Thai Grapheme-to-Phoneme conversion is proposed to tackle this problem. This paper presents an application of English syllable features to tone prediction model which is capable of classifying ambiguous tones and a refinement of post-processing module for phone prediction model. Experimental results show that the syllable and word accuracy are 76.03% and 53.93% respectively, which outperform all previous models.

关键词： Predictive models Accuracy Analytical models Hidden Markov models Testing Mathematical model Dictionaries

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