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12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Garera, Nikesh Yarowsky, David Department of Computer Science Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD United States

ISBN: (纸本)9781932432169

This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent properties of biographical data. The ensemble of these proposed models substantially outperforms standard pattern-based biographic fact extraction methods and performance is further improved by modeling inter-attribute correlations and distributions over functions of attributes, achieving an average extraction accuracy of 80% over seven types of biographic attributes. © 2009 Association for Computational Linguistics.

关键词： Extraction

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Word sense disambiguation vs. statistical machine translation 05

Word sense disambiguation vs. statistical machine translatio...

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43rd Annual Meeting of the Association for Computational Linguistics, ACL-05

作者： Carpuat, Marine Wu, Dekai Department of Computer Science Human Language Technology Center University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)1932432515

We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translation quality? We present empirical results casting doubt on this common, but unproved, assumption. Using a state-ofthe- art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures. © 2005 Association for Computational Linguistics.

关键词： computer aided language translation

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Modeling Latent Biographic Attributes in Conversational Genres

Modeling Latent Biographic Attributes in Conversational Genr...

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Garera, Nikesh Yarowsky, David Department of Computer Science Johns Hopkins University Human Language Technology Center of Excellence BaltimoreMD United States

ISBN: (纸本)9781617382581

This paper presents and evaluates several original techniques for the latent classification of biographic attributes such as gender, age and native language, in diverse genres (conversation transcripts, email) and languages (Arabic, English). First, we present a novel partner-sensitive model for extracting biographic attributes in conversations, given the differences in lexical usage and discourse style such as observed between same-gender and mixed-gender conversations. Then, we explore a rich variety of novel sociolinguistic and discourse-based features, including mean utterance length, passive/active usage, percentage domination of the conversation, speaking rate and filler word usage. Cumulatively up to 20% error reduction is achieved relative to the standard Boulis and Ostendorf (2005) algorithm for classifying individual conversations on Switchboard, and accuracy for gender detection on the Switchboard corpus (aggregate) and Gulf Arabic corpus exceeds 95%. © 2009 ACL and AFNLP.

关键词： Natural language processing systems

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A grammatical approach to understanding textual tables using two-dimensional SCFGs 21

A grammatical approach to understanding textual tables using...

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21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006

作者： Wu, Dekai Lee, Ken Wing Kuen Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong

We present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. Unlike the few existing table analysis models, which largely rely on relatively ad hoc heuristics, our linguistically-oriented approach is systematic and grammar based, which allows our model (1) to be concise and yet (2) recognize a wider range of data models than others, and (3) disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. To accomplish this, the model introduces Viterbi parsing under two-dimensional stochastic CFGs. The cleaner grammatical approach facilitates not only greater coverage, but also grammar extension and maintenance, as well as a more direct and declarative link to semantic interpretation, for which we also introduce a new, cleaner data model. In disambiguation experiments on recognizing relevant data models of unseen web tables from different domains, a blind evaluation of the model showed 60% precision and 80% recall. © 2006 Association for Computational Linguistics

关键词： Stochastic systems

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Learning to freestyle: Hip hop challenge-response induction via transduction rule segmentation

Learning to freestyle: Hip hop challenge-response induction ...

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2013 Conference on Empirical Methods in Natural language Processing, EMNLP 2013

作者： Wu, Dekai Addanki, Karteek Saers, Markus Beloucif, Meriem Human Language Technology Center Department of Computer Science HKUST Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781937284978

We present a novel model, Freestyle, that learns to improvise rhyming and fluent responses upon being challenged with a line of hip hop lyrics, by combining both bottom-up token based rule induction and top-down rule segmentation strategies to learn a stochastic transduction grammar that simultaneously learns both phrasing and rhyming associations. In this attack on the woefully under-explored natural language genre of music lyrics, we exploit a strictly unsupervised transduction grammar induction approach. Our task is particularly ambitious in that no use of any a priori linguistic or phonetic information is allowed, even though the domain of hip hop lyrics is particularly noisy and unstructured. We evaluate the performance of the learned model against a model learned only using the more conventional bottom-up token based rule induction, and demonstrate the superiority of our combined token based and rule segmentation induction method toward generating higher quality improvised responses, measured on fluency and rhyming criteria as judged by human evaluators. To highlight some of the inherent challenges in adapting other algorithms to this novel task, we also compare the quality of the responses generated by our model to those generated by an out-of-the-box phrase based SMT system. We tackle the challenge of selecting appropriate training data for our task via a dedicated rhyme scheme detection module, which is also acquired via unsupervised learning and report improved quality of the generated responses. Finally, we report results with Maghrebi French hip hop lyrics indicating that our model performs surprisingly well with no special adaptation to other languages. © 2013 Association for Computational Linguistics.

关键词： Stochastic systems

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Zara: A virtual interactive dialogue system incorporating emotion, sentiment and personality recognition 26

Zara: A virtual interactive dialogue system incorporating em...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Fung, Pascale Dey, Anik Bin Siddique, Farhad Lin, Ruixi Yang, Yang Bertero, Dario Yan, Wan Yin, Ricky Chan Ho Wu, Chien-Sheng Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9784879747037

Zara, or 'Zara the Supergirl' is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7% accuracy on six different emotion classes, which is an impressive 4.5% improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues. © COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: System Demonstrations.

关键词： Speech recognition

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ON THE USE OF RIGHT CONTEXT IN SENSE-DISAMBIGUATING language MODELS 6

ON THE USE OF RIGHT CONTEXT IN SENSE-DISAMBIGUATING LANGUAGE...

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6th European Conference on Speech Communication and Technology, EUROSPEECH 1999

作者： Chow, Vincent Wu, Dekai Human Language Technology Center Department of Computer Science HKUST Clear Water Bay Hong Kong Hong Kong

We investigate the utility of right-context (look-ahead information) in incremental left-to-right language models with word sense disambiguation, and discover somewhat unexpectedly that using right-context in addition to left-context (history) may actually reduce accuracy. We employ word sense disambiguation as one component of a language model designed to allow hypothesis to be evaluated incrementally. In our baseline system, disambiguation is performed by a na'ive-Bayes classifier that uses lexical co-occurrence features from the history. We then augment the left-context only model with three well-motivated methods using the right-context. Perhaps surprisingly, experiment results with the three look-ahead strategies shown a 0.19% up to 10.04% decrease in the accuracy of disambiguating the next word. © 1999 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999. All rights reserved.

关键词： Computational linguistics

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting 29

DP-Rewrite: Towards Reproducibility and Transparency in Diff...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Transparency

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One-step and two-step classification for abusive language detection on twitter 1

One-step and two-step classification for abusive language de...

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1st Workshop on Abusive language Online, ALW 2017 at the 55th Annual Meeting of the Association for Computational Linguistic, ACL 2017 - Proceedings of the Workshop

作者： Park, Ji Ho Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781945626661

Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps. © 2017 Association for Computational Linguistics

关键词： Classification (of information)

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Treatment Fidelity in a Feasibility Trial of the Aphasia Intervention, Virtual Elaborated Semantic Feature Analysis

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International Journal of language & Communication Disorders 2025年第3期60卷 e70054页

作者： Niamh Devane Sofia Mazzoleni Nicholas Behn Jane Marshall Stephanie Wilson Katerina Hilari Department of Language and Communication Science City St George's University of London London UK East London NHS Foundation Trust London UK Centre for Human-Computer Interaction Design City St George's University of London London UK

Background and Aims The reliability and validity of an intervention can be improved by checking treatment fidelity (TF). TF methods identify core components of an intervention, check their presence (or absence) and identify threats to fidelity. The Virtual Elaborated Semantic Feature Analysis (VESFA) intervention comprised individual sessions of word-finding treatment and group sessions of conversation practice. All sessions were delivered in the virtual world of EVA Park. This paper describes the TF in the VESFA trial that explored (1) if the treatment was delivered as planned, (2) which components influenced treatment adherence scores and (3) the reliability of the fidelity checklists. Methods and Procedures Strategies to improve fidelity were employed in the study design, the delivery of treatment, treatment receipt and treatment enactment. Two fidelity checklists were developed with input from advisors with aphasia to establish the core components of the intervention (individual and group). During the trial, treatment sessions were video-recorded. A sample of 20% of sessions was randomly selected for adherence rating. Seven research students were trained to rate the videos using the fidelity checklists. Inter- and intra-rater reliability was established. Outcomes and Results Study design strategies ensured 94% of sessions ran as planned and 75% of participants (12/16) received over 90% (>36/40h) of the intended dose. The average TF across all sessions rated was 81%, demonstrating a high degree of fidelity in the delivery of the VESFA intervention. The fidelity of the individual sessions was lower (78%) than the group elements (84%). The components that most threatened treatment adherence were (1) providing a rationale for the activities and (2) specific feedback for performance. Nevertheless, participants consistently practised target words both in individual sessions and in conversations in the group sessions, demonstrating treatment receipt. Ninety-four percen

关键词： aphasia treatment validity

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