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Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models 2

Drug Extraction from the Web: Summarizing Drug Experiences w...

引用

2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013

作者： Paul, Michael J. Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

Multi-dimensional latent text models, such as factorial LDA (f-LDA), capture multiple factors of corpora, creating structured output for researchers to better understand the contents of a corpus. We consider such models for clinical research of new recreational drugs and trends, an important application for mining current information for healthcare workers. We use a "three-dimensional" f-LDA variant to jointly model combinations of drug (marijuana, salvia, etc.), aspect (effects, chemistry, etc.) and route of administration (smoking, oral, etc.) Since a purely unsupervised topic model is unlikely to discover these specific factors of interest, we develop a novel method of incorporating prior knowledge by leveraging user generated tags as priors in our model. We demonstrate that this model can be used as an exploratory tool for learning about these drugs from the Web by applying it to the task of extractive summarization. In addition to providing useful output for this important public health task, our prior-enriched model provides a framework for the application of f-LDA to other tasks. © 2013 Association for Computational Linguistics.

关键词： Clinical research

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Carmen: A twitter geolocation system with applications to public health

Carmen: A twitter geolocation system with applications to pu...

引用

2013 AAAI Workshop

作者： Dredze, Mark Paul, Michael J. Bergsma, Shane Tran, Hieu Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21211 United States

ISBN: (纸本)9781577356202

Public health applications using social media often require accurate, broad-coverage location information. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data. We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Public health

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Drug extraction from the web: Summarizing drug experiences with multi-dimensional topic models

Drug extraction from the web: Summarizing drug experiences w...

引用

2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

作者： Paul, Michael J. Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

Multi-dimensional latent text models, such as factorial LDA (f-LDA), capture multiple factors of corpora, creating structured output for researchers to better understand the contents of a corpus. We consider such models for clinical research of new recreational drugs and trends, an important application for mining current information for healthcare workers. We use a "three-dimensional" f-LDA variant to jointly model combinations of drug (marijuana, salvia, etc.), aspect (effects, chemistry, etc.) and route of administration (smoking, oral, etc.) Since a purely unsupervised topic model is unlikely to discover these specific factors of interest, we develop a novel method of incorporating prior knowledge by leveraging user generated tags as priors in our model. We demonstrate that this model can be used as an exploratory tool for learning about these drugs from the Web by applying it to the task of extractive summarization. In addition to providing useful output for this important public health task, our prior-enriched model provides a framework for the application of f- LDA to other tasks. © 2013 Association for Computational Linguistics.

关键词： Clinical research

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UNSUPERVISED IDIOLECT DISCOVERY FOR SPEAKER RECOGNITION

UNSUPERVISED IDIOLECT DISCOVERY FOR SPEAKER RECOGNITION

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： Aren Jansen Daniel Garcia-Romero Pascal Clark Jaime Hernandez-Cordero Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA U.S. Department of Defense

ISBN: (纸本)9781479928941

Short-time spectral characterizations of the human voice have proven to be the most dependable features available to modern speaker recognition systems. However, it is well-known that high-level linguistic information such as word usage and pronunciation patterns can provide complementary discriminative power. In an automatic setting, the availability of these idiolectal cues is dependent on access to a word or phonetic tokenizer, ideally in the given language and domain. In this paper, we propose a novel approach to speaker recognition that leverages recently developed zero-resource term discovery algorithms to identify speaker-characteristic lexical and phrasal acoustic patterns without the need for any supervised speech recognition tools. We use the enrollment audio itself to score each trial and perform no model training (supervised or unsupervised) at any stage of the processing, allowing immediate application to any language or domain. We evaluate our approach on the extended 8-conversation core condition of the 2010 NIST SRE and demonstrate a 16% relative (0.06 absolute) reduction in minDCF when combined with a state-of-the-art unsupervised i-vector cosine system.

关键词： speaker recognition Loudspeakers Word language recognition system

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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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Explicit and implicit syntactic features for text classification

Explicit and implicit syntactic features for text classifica...

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

作者： Post, Matt Bergsma, Shane Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Center for Language and Speech Processing Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781937284510

Syntactic features are useful for many text classification tasks. Among these, tree kernels (Collins and Duffy, 2001) have been perhaps the most robust and effective syntactic tool, appealing for their empirical success, but also because they do not require an answer to the difficult question of which tree features to use for a given task. We compare tree kernels to different explicit sets of tree features on five diverse tasks, and find that explicit features often perform as well as tree kernels on accuracy and always in orders of magnitude less time, and with smaller models. Since explicit features are easy to generate and use (with publicly available tools), we suggest they should always be included as baseline comparisons in tree kernel method evaluations. © 2013 Association for Computational Linguistics.

关键词： Syntactics

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Exploring demographic language variations to improve multilingual sentiment analysis in social media

Exploring demographic language variations to improve multili...

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

作者： Volkova, Svitlana Wilson, Theresa Yarowsky, David Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9781937284978

Different demographics, e.g., gender or age, can demonstrate substantial variation in their language use, particularly in informal contexts such as social media. In this paper we focus on learning gender differences in the use of subjective language in English, Spanish, and Russian Twitter data, and explore cross-cultural differences in emoticon and hashtag use for male and female users. We show that gender differences in subjective language can effectively be used to improve sentiment analysis, and in particular, polarity classification for Spanish and Russian. Our results show statistically significant relative F-measure improvement over the gender-independent baseline 1.5% and 1% for Russian, 2% and 0.5% for Spanish, and 2.5% and 5% for English for polarity and subjectivity classification. © 2013 Association for Computational Linguistics.

关键词： Sentiment analysis

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Joshua 5.0: Sparser, better, faster, server 8

Joshua 5.0: Sparser, better, faster, server

引用

8th Workshop on Statistical Machine Translation, WMT 2013

作者： Post, Matt Ganitkevitch, Juri Orland, Luke Weese, Jonathan Cao, Yuan Callison-Burch, Chris Human Language Technology Center of Excellence Johns Hopkins University Center for Language and Speech Processing Johns Hopkins University Computer and Information Sciences Department University of Pennsylvania United States

ISBN: (纸本)9781937284572

We describe improvements made over the past year to Joshua, an open-source translation system for parsing-based machine translation. The main contributions this past year are significant improvements in both speed and usability of the grammar extraction and decoding steps. We have also rewritten the decoder to use a sparse feature representation, enabling training of large numbers of features with discriminative training methods. © 2013 Association for Computational Linguistics

关键词： Open systems

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Modeling predictive perceptual representation of Thai initial consonants

Modeling predictive perceptual representation of Thai initia...

引用

Oriental COCOSDA International Conference on speech Database and Assessments

作者： P. Phienphanich C. Onsuwan C. Tantibundhit N. Saimai T. Saimai Department of Electrical and Computer Engineering Faculty of Engineering Department of Linguistics Faculty of Liberal Arts Center of Excellence in Intelligent Informatics Speech and Language Technology and Service Innovation (CILS) Thammasat University Pathumthani Thailand

This work is an extension of our previous attempt to construct a spatial representation of 21 initial consonants in Thai by partitioning them into homogeneous clusters based on empirical measures of confusability and distance among phonemes. The measures were taken from perceptual identification performance of 28 listeners (seven full subjects) when stimuli were presented in noise. In present study, two methods of clustering, namely Multidimensional scaling analysis and k-means clustering were employed, yielding six different classifications and four perceptually relevant categories: intra-cluster short distance, intra-cluster long distance, inter-cluster short distance, and inter-cluster long distance. Another set of perceptual experiment (eight listeners; two full subjects) was carried out to verify the predictions. The findings reveal that the derived perceptual clusters and defined categories fit relatively well with the listeners' performance. Distinctive feature systems in phonological theory appear to provide some basis for the clustering of phonemes.

关键词： System-on-chip Noise measurement Noise Psychology speech

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PPDB: The paraphrase database

PPDB: The paraphrase database

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

作者： Ganitkevitch, Juri Van Durme, Benjamin Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781937284473

We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 140 million paraphrase patterns, which capture many meaning-preserving syntactic transformations. The paraphrases are extracted from bilingual parallel corpora totaling over 100 million sentence pairs and over 2 billion English words. We also release PPDB:Spa, a collection of 196 million Spanish paraphrases. Each paraphrase pair in PPDB contains a set of associated scores, including paraphrase probabilities derived from the bitext data and a variety of monolingual distributional similarity scores computed from the Google n-grams and the Annotated Gigaword corpus. Our release includes pruning tools that allow users to determine their own precision/recall tradeoff. © 2013 Association for Computational Linguistics.

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