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11th Linguistic Annotation Workshop, LAW XI 2017

作者： Hartmann, Silvana Mújdricza-Maydt, Éva Kuznetsov, Ilia Gurevych, Iryna Frank, Anette Ubiquitous Knowledge Processing Lab Department of Computer Science Technische Universität Darmstadt Germany Research Training School AIPHES Germany Department of Computational Linguistics Heidelberg University Germany

ISBN: (纸本)9781945626395

We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet. © 2017 Association for Computational Linguistics.

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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On the linearity of semantic change: Investigating meaning variation via dynamic graph models

arXiv

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arXiv 2017年

作者： Eger, Steffen Mehler, Alexander Ubiquitous Knowledge Processing Lab Department of Computer Science Technische Universität Darmstadt Germany Text Technology Lab Department of Computer Science Goethe-Universität Frankfurt am Main Germany

We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word's meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today's embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change. Copyright © 2017, The Authors. All rights reserved.

关键词： Embeddings

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Modeling extractive sentence intersection via subtree entailment 26

Modeling extractive sentence intersection via subtree entail...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Levy, Omer Dagan, Ido Stanovsky, Gabriel Eckle-Kohler, Judith Gurevych, Iryna Computer Science Department Bar-Ilan University Israel Ubiquitous Knowledge Processing Lab. Technische Universität Darmstadt Germany Ubiquitous Knowledge Processing Lab. German Institute for Educational Research Germany

ISBN: (纸本)9784879747020

Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections. © 1963-2018 ACL.

关键词： Semantics

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A novel attack model for collusion secure fingerprinting codes

A novel attack model for collusion secure fingerprinting cod...

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Media Watermarking, Security, and Forensics 2016

作者： Schäfer, Marcel Berchtold, Waldemar Stark, Teetje Reimers, Nils Steinebach, Martin Fraunhofer-Institute SIT Darmstadt Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Ubiquitous Knowledge Processing Lab - Department of Computer Science Technische Universitat Darmstadt Darmstadt Germany

Probabilistic or bias-based fingerprinting codes to counter collusion attacks are applied to enhance the security of transaction watermarking applications. Into every media copy to be distributed is embedded a unique fingerprint via watermarking techniques. With it a distributor is able to trace back unauthorized redistributed versions to its source with a high probability even if the media was subject to a collusion attack. The seminal Tardos fingerprinting codes and all its derivatives rely on position independent fingerprints. Hence in most work it is assumed that the attackers also rely on the position independence when creating the media forgery containing a manipulated fingerprint. However, they need not follow this assumption. In this work we present a novel iterative attack model that does not rely on the position independence but could be applied in practice. The corresponding attacks iteratively adapt the manipulated fingerprint with the intention to maximally reduce their accusation scores in order to escape the accusation. For practical collusion sizes, the attacks show better performance than other attacks typically discussed in literature. In other words, the attacks result in manipulated fingerprints that lead to higher error rates of the fingerprinting scheme, compared to the attacks discussed in literature. © 2016 Society for Imaging science and Technology.

关键词： Iterative methods

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Crossmodal language grounding, learning, and teaching

Crossmodal language grounding, learning, and teaching

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2016 Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches, CoCo 2016

作者： Heinrich, Stefan Weber, Cornelius Wermter, Stefan Xie, Ruobing Lin, Yankai Liu, Zhiyuan Department of Informatics Knowledge Technology Group University of Hamburg Vogt-Koelln Str. 30 Hamburg22527 Germany Department of Computer Science and Technology Natural Language Processing Lab Tsinghua University Room 4-506 FIT Building Beijing100084 China

The human brain as one of the most complex dynamic systems enables us to communicate and externalise information by natural language. Despite extensive research, human-like communication with interactive robots is not yet possible, because we have not yet fully understood the mechanistic characteristics of the crossmodal binding between language, actions, and visual sensation that enable humans to acquire and use natural language. In this position paper we present vision- and action-embodied language learning research as part of a project investigating multi-modal learning. Our research endeavour includes to develop a) a cortical neural-network model that learns to ground language into crossmodal embodied perception and b) a knowledge-based teaching framework to bootstrap and scale-up the language acquisition to a level of language development in children of age up to two years. We embed this approach of internally grounding embodied experience and externally teaching abstract experience into the developmental robotics paradigm, by means of developing and employing a neurorobot that is capable of multisensory perception and interaction. The proposed research contributes to designing neuroscientific experiments on discovering crossmodal integration particularly in language processing and to constructing future robotic companions capable of natural communication. Copyright © 2016 for this paper by its authors.

关键词： knowledge based systems

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words 6

Analysing domain suitability of a sentiment lexicon by ident...

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6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2015 at the 2015 Conference on Empirical Methods in Natural Language processing, EMNLP 2015

作者： Flekova, Lucie Ruppert, Eugen Preoţiuc-Pietro, Daniel Ubiquitous Knowledge Processing Lab Technische Universität Darmstadt Germany FG Language Technology Technische Universität Darmstadt Germany Computer and Information Science University of Pennsylvania United States

ISBN: (纸本)9781941643327

Contemporary sentiment analysis approaches rely heavily on lexicon based methods. This is mainly due to their simplicity, although the best empirical results can be achieved by more complex techniques. We introduce a method to assess suitability of generic sentiment lexicons for a given domain, namely to identify frequent bigrams where a polar word switches polarity. Our bigrams are scored using Lexicographers Mutual Information and leveraging large automatically obtained corpora. Our score matches human perception of polarity and demonstrates improvements in classification results using our enhanced context-aware method. Our method enhances the assessment of lexicon based sentiment detection algorithms and can be further used to quantify ambiguous words. © 2015 Association for Computational Linguistics.

关键词： Sentiment analysis

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Automatically assigning research methods to journal articles in the domain of social sciences

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Proceedings of the Association for Information science and Technology 2014年第1期50卷 1-8页

作者： Judith Eckle-Kohler Tri-Duc Nghiem Iryna Gurevych Ubiquitous Knowledge Processing (UKP) Lab Department of Computer Science Technische Universität Darmstadt Germany

We investigate the automatic assignment of research methods to journal articles from the domain of Social sciences. We employ computer science and Computational Linguistics methodology to perform this automatic assignment of metadata. The multi-label classification system we present uses only abstracts and titles of journal articles as input. Our best system is able to assign the important research methods empirical and quantitative empirical with F-scores of 0.67 and 0.68. These research methods are in the focus of many recent manual analyses of publications databases. Our classification approach could be applied to automatically analyze large publications databases and databases of bibliographic references according to the use of empirical and quantitative empirical methods.

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