Publikationen

2019

  • J. Frey, K. Müller, S. Hellmann, E. Rahm, and M. -, „Evaluation of metadata representations in RDF stores,“ Semantic Web, vol. 10, iss. 2, p. 205–229, 2019. doi:10.3233/SW-180307
    [BibTeX] [Download PDF]
    @article{DBLP:journals/semweb/FreyMHRV19,
    author = {Johannes Frey and
    Kay M{\"{u}}ller and
    Sebastian Hellmann and
    Erhard Rahm and
    Maria{-}Esther Vidal},
    title = {Evaluation of metadata representations in {RDF} stores},
    journal = {Semantic Web},
    volume = {10},
    number = {2},
    pages = {205--229},
    year = {2019},
    url = {https://doi.org/10.3233/SW-180307},
    doi = {10.3233/SW-180307},
    timestamp = {Sat, 09 Feb 2019 14:41:15 +0100},
    biburl = {https://dblp.org/rec/bib/journals/semweb/FreyMHRV19},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

2018

  • S. Bosch, T. Eckart, B. Klimek, D. Goldhahn, and U. Quasthoff, „Preparation and Usage of Xhosa Lexicographical Data for a Multilingual, Federated Environment,“ in Proceedings of the 11th Language Resources and Evaluation Conference, Miyazaki, Japan, 2018.
    [BibTeX] [Download PDF]
    @inproceedings{bosch-etal-2018-preparation,
    title = "Preparation and Usage of {X}hosa Lexicographical Data for a Multilingual, Federated Environment",
    author = "Bosch, Sonja and
    Eckart, Thomas and
    Klimek, Bettina and
    Goldhahn, Dirk and
    Quasthoff, Uwe",
    booktitle = "Proceedings of the 11th Language Resources and Evaluation Conference",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/L18-1692",
    }

  • D. Fernandez-Alvarez, H. Garcia-Gonzalez, J. Frey, S. Hellmann, and J. E. L. Gayo, „Inference of Latent Shape Expressions Associated to DBpedia Ontology,“ in Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th – to – 12th, 2018., 2018.
    [BibTeX] [Download PDF]
    @inproceedings{DBLP:conf/semweb/Fernandez-Alvarez18,
    author = {Daniel Fernandez-Alvarez and
    Herminio Garcia-Gonzalez and
    Johannes Frey and
    Sebastian Hellmann and
    Jose Emilio Labra Gayo},
    title = {Inference of Latent Shape Expressions Associated to DBpedia Ontology},
    booktitle = {Proceedings of the {ISWC} 2018 Posters {\&} Demonstrations, Industry
    and Blue Sky Ideas Tracks co-located with 17th International Semantic
    Web Conference {(ISWC} 2018), Monterey, USA, October 8th - to - 12th,
    2018.},
    year = {2018},
    crossref = {DBLP:conf/semweb/2018p},
    url = {http://ceur-ws.org/Vol-2180/paper-15.pdf},
    timestamp = {Thu, 11 Oct 2018 12:45:11 +0200},
    biburl = {https://dblp.org/rec/bib/conf/semweb/Fernandez-Alvarez18},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • P. Gupta, Y. Chaudhary, F. Buettner, and H. Schütze, „Document Informed Neural Autoregressive Topic Models with Distributional Prior,“ CoRR, vol. abs/1809.06709, 2018.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/abs-1809-06709,
    author = {Pankaj Gupta and
    Yatin Chaudhary and
    Florian Buettner and
    Hinrich Sch{\"{u}}tze},
    title = {Document Informed Neural Autoregressive Topic Models with Distributional
    Prior},
    journal = {CoRR},
    volume = {abs/1809.06709},
    year = {2018},
    url = {http://arxiv.org/abs/1809.06709},
    archivePrefix = {arXiv},
    eprint = {1809.06709},
    timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1809-06709},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • P. Gupta, B. Roth, and H. Schütze, „Joint Bootstrapping Machines for High Confidence Relation Extraction,“ in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, p. 26–36.
    [BibTeX] [Download PDF]
    @InProceedings{N18-1003,
    author = "Gupta, Pankaj
    and Roth, Benjamin
    and Sch{\"u}tze, Hinrich",
    title = "Joint Bootstrapping Machines for High Confidence Relation Extraction",
    booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    year = "2018",
    publisher = "Association for Computational Linguistics",
    pages = "26--36",
    location = "New Orleans, Louisiana",
    url = "http://aclweb.org/anthology/N18-1003"
    }

  • P. Gupta, B. Andrassy, and H. Schütze, „Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts,“ in Proceedings of the Third Workshop on Semantic Deep Learning, Santa Fe, New Mexico, 2018, p. 1–11.
    [BibTeX] [Abstract] [Download PDF]

    The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22{\%} and 7{\%} gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.

    @inproceedings{gupta-etal-2018-replicated,
    title = "Replicated {S}iamese {LSTM} in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts",
    author = {Gupta, Pankaj and
    Andrassy, Bernt and
    Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4001",
    pages = "1--11",
    abstract = "The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22{\%} and 7{\%} gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.",
    }

  • P. Gupta, S. Rajaram, H. Schütze, B. Andrassy, and T. A. Runkler, „Neural Relation Extraction Within and Across Sentence Boundaries,“ CoRR, vol. abs/1810.05102, 2018.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/abs-1810-05102,
    author = {Pankaj Gupta and
    Subburam Rajaram and
    Hinrich Sch{\"{u}}tze and
    Bernt Andrassy and
    Thomas A. Runkler},
    title = {Neural Relation Extraction Within and Across Sentence Boundaries},
    journal = {CoRR},
    volume = {abs/1810.05102},
    year = {2018},
    url = {http://arxiv.org/abs/1810.05102},
    archivePrefix = {arXiv},
    eprint = {1810.05102},
    timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1810-05102},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • P. Gupta and H. Schütze, „LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation,“ in Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Brussels, Belgium, 2018, p. 154–164.
    [BibTeX] [Abstract] [Download PDF]

    Recurrent neural networks (RNNs) are temporal

    @inproceedings{gupta-schutze-2018-lisa,
    title = "{LISA}: Explaining Recurrent Neural Network Judgments via Layer-w{I}se Semantic Accumulation and Example to Pattern Transformation",
    author = {Gupta, Pankaj and
    Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5418",
    pages = "154--164",
    abstract = "Recurrent neural networks (RNNs) are temporal",
    }

  • P. Gupta, S. Rajaram, H. Schütze, and B. Andrassy, „Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time,“ in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, p. 1079–1089.
    [BibTeX] [Download PDF]
    @InProceedings{N18-1098,
    author = "Gupta, Pankaj
    and Rajaram, Subburam
    and Sch{\"u}tze, Hinrich
    and Andrassy, Bernt",
    title = "Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time",
    booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    year = "2018",
    publisher = "Association for Computational Linguistics",
    pages = "1079--1089",
    location = "New Orleans, Louisiana",
    url = "http://aclweb.org/anthology/N18-1098"
    }

  • T. Kilias, A. Löser, F. A. Gers, R. Koopmanschap, Y. Zhang, and M. L. Kersten, „IDEL: In-Database Entity Linking with Neural Embeddings,“ CoRR, vol. abs/1803.04884, 2018.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/abs-1803-04884,
    author = {Torsten Kilias and
    Alexander L{\"{o}}ser and
    Felix A. Gers and
    Richard Koopmanschap and
    Ying Zhang and
    Martin L. Kersten},
    title = {{IDEL:} In-Database Entity Linking with Neural Embeddings},
    journal = {CoRR},
    volume = {abs/1803.04884},
    year = {2018},
    url = {http://arxiv.org/abs/1803.04884},
    archivePrefix = {arXiv},
    eprint = {1803.04884},
    timestamp = {Mon, 13 Aug 2018 16:46:19 +0200},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1803-04884},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • J. Kirschnick, P. Thomas, R. Roller, and L. Hennig, „SIA: a scalable interoperable annotation server for biomedical named entities,“ J. Cheminformatics, vol. 10, iss. 1, p. 63:1–63:7, 2018. doi:10.1186/s13321-018-0319-2
    [BibTeX] [Download PDF]
    @article{DBLP:journals/jcheminf/KirschnickTRH18,
    author = {Johannes Kirschnick and
    Philippe Thomas and
    Roland Roller and
    Leonhard Hennig},
    title = {{SIA:} a scalable interoperable annotation server for biomedical named
    entities},
    journal = {J. Cheminformatics},
    volume = {10},
    number = {1},
    pages = {63:1--63:7},
    year = {2018},
    url = {https://doi.org/10.1186/s13321-018-0319-2},
    doi = {10.1186/s13321-018-0319-2},
    timestamp = {Wed, 26 Dec 2018 19:52:06 +0100},
    biburl = {https://dblp.org/rec/bib/journals/jcheminf/KirschnickTRH18},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • B. Klimek, R. Schädlich, D. Kröger, E. Knese, and B. Elßmann, „LiDo RDF: From a Relational Database to a Linked Data Graph of Linguistic Terms and Bibliographic Data,“ in Proceedings of the 11th Language Resources and Evaluation Conference, Miyazaki, Japan, 2018.
    [BibTeX] [Download PDF]
    @inproceedings{klimek-etal-2018-lido,
    title = "{L}i{D}o {RDF}: From a Relational Database to a Linked Data Graph of Linguistic Terms and Bibliographic Data",
    author = {Klimek, Bettina and
    Sch{\"a}dlich, Robert and
    Kr{\"o}ger, Dustin and
    Knese, Edwin and
    El{\ss}mann, Benedikt},
    booktitle = "Proceedings of the 11th Language Resources and Evaluation Conference",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/L18-1386",
    }

  • S. Krause, „Knowledge-Intensive, High-Performance Relation Extraction,“ PhD Thesis, 2018.
    [BibTeX]
    @phdthesis{krause_knowledge-intensive_2018,
    title = {Knowledge-{Intensive}, {High}-{Performance} {Relation} {Extraction}},
    school = {TU Berlin},
    author = {Krause, Sebastian},
    year = {2018}
    }

  • G. C. Publio, D. Esteves, A. Lawrynowicz, P. Panov, L. N. Soldatova, T. Soru, J. Vanschoren, and H. Zafar, „ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies,“ CoRR, vol. abs/1807.05351, 2018.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/abs-1807-05351,
    author = {Gustavo Correa Publio and
    Diego Esteves and
    Agnieszka Lawrynowicz and
    Pance Panov and
    Larisa N. Soldatova and
    Tommaso Soru and
    Joaquin Vanschoren and
    Hamid Zafar},
    title = {ML-Schema: Exposing the Semantics of Machine Learning with Schemas
    and Ontologies},
    journal = {CoRR},
    volume = {abs/1807.05351},
    year = {2018},
    url = {http://arxiv.org/abs/1807.05351},
    archivePrefix = {arXiv},
    eprint = {1807.05351},
    timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
    biburl = {https://dblp.org/rec/bib/journals/corr/abs-1807-05351},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

  • G. C. Publio, „SHARK: A Test-Driven Framework for Design and Evolution of Ontologies,“ in The Semantic Web: ESWC 2018 Satellite Events, Cham, 2018, p. 314–324.
    [BibTeX] [Abstract]

    In the Semantic Web, the sharing and reuse of knowledge are made possible by ontologies which establish common vocabularies and semantic interpretations of terms. Over the last years, the LOD cloud has been growing substantially in size of each ontology and the total number of objects. In order to ensure a certain level of data quality, several methods have been proposed so far, with different characteristics and approaches, demanding the composition of different tools to ensure a full validation and continuous integration with hosting solutions. In this paper, we present SHARK, a single framework capable to test an ontology using formally pre-defined guidelines or custom SHACL tests, and can also be used for continuous testing during the ontology development process.

    @InProceedings{10.1007/978-3-319-98192-5_50,
    author="Publio, Gustavo Correa",
    editor="Gangemi, Aldo
    and Gentile, Anna Lisa
    and Nuzzolese, Andrea Giovanni
    and Rudolph, Sebastian
    and Maleshkova, Maria
    and Paulheim, Heiko
    and Pan, Jeff Z
    and Alam, Mehwish",
    title="SHARK: A Test-Driven Framework for Design and Evolution of Ontologies",
    booktitle="The Semantic Web: ESWC 2018 Satellite Events",
    year="2018",
    publisher="Springer International Publishing",
    address="Cham",
    pages="314--324",
    abstract="In the Semantic Web, the sharing and reuse of knowledge are made possible by ontologies which establish common vocabularies and semantic interpretations of terms. Over the last years, the LOD cloud has been growing substantially in size of each ontology and the total number of objects. In order to ensure a certain level of data quality, several methods have been proposed so far, with different characteristics and approaches, demanding the composition of different tools to ensure a full validation and continuous integration with hosting solutions. In this paper, we present SHARK, a single framework capable to test an ontology using formally pre-defined guidelines or custom SHACL tests, and can also be used for continuous testing during the ontology development process.",
    isbn="978-3-319-98192-5"
    }

  • M. Schiersch, V. Mironova, M. Schmitt, P. Thomas, A. Gabryszak, and L. Hennig, „A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events,“ in Proceedings of the 10th International Conference on Language Resources and Evaluation, Miyazaki, Japan, 2018.
    [BibTeX]
    @inproceedings{schiersch2018,
    title = {A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events},
    author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
    booktitle = {Proceedings of the 10th International Conference on Language Resources and Evaluation},
    year = {2018},
    address = {Miyazaki, Japan},
    publisher = {European Language Resources Association}
    }

  • S. Schön, V. Mironova, A. Gabryszak, and L. Hennig, „A Corpus Study and Annotation Schema for Named Entity Recognition of Business Products,“ in Proceedings of the 10th International Conference on Language Resources and Evaluation, Miyazaki, Japan, 2018.
    [BibTeX]
    @inproceedings{schoen2018,
    title = {A Corpus Study and Annotation Schema for Named Entity Recognition of Business Products},
    author = {Saskia Sch\"{o}n and Veselina Mironova and Aleksandra Gabryszak and Leonhard Hennig},
    booktitle = {Proceedings of the 10th International Conference on Language Resources and Evaluation},
    year = {2018},
    address = {Miyazaki, Japan},
    publisher = {European Language Resources Association}
    }

2017

  • K. Eichler, F. Xu, H. Uszkoreit, and S. Krause, „Generating Pattern-Based Entailment Graphs for Relation Extraction,“ in Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Vancouver, Canada, 2017, p. 220–229.
    [BibTeX] [Abstract] [Download PDF]

    Relation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions. Deriving relation patterns automatically usually results in large numbers of candidates, which need to be filtered to derive a subset of patterns that reliably extract correct relation mentions. We address the pattern selection task by exploiting the knowledge represented by entailment graphs, which capture semantic relationships holding among the learned pattern candidates. This is motivated by the fact that a pattern may not express the target relation explicitly, but still be useful for extracting instances for which the relation holds, because its meaning entails the meaning of the target relation. We evaluate the usage of both automatically generated and gold-standard entailment graphs in a relation extraction scenario and present favorable experimental results, exhibiting the benefits of structuring and selecting patterns based on entailment graphs.

    @InProceedings{eichler-EtAl:2017:starSEM,
    author = {Eichler, Kathrin and Xu, Feiyu and Uszkoreit, Hans and Krause, Sebastian},
    title = {Generating Pattern-Based Entailment Graphs for Relation Extraction},
    booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
    month = {August},
    year = {2017},
    address = {Vancouver, Canada},
    publisher = {Association for Computational Linguistics},
    pages = {220--229},
    abstract = {Relation extraction is the task of recognizing and extracting relations between
    entities or concepts in texts. A common approach is to exploit existing
    knowledge to learn linguistic patterns expressing the target relation and use
    these patterns for extracting new relation mentions. Deriving relation patterns
    automatically usually results in large numbers of candidates, which need to be
    filtered to derive a subset of patterns that reliably extract correct relation
    mentions. We address the pattern selection task by exploiting the knowledge
    represented by entailment graphs, which capture semantic relationships holding
    among the learned pattern candidates. This is motivated by the fact that a
    pattern may not express the target relation explicitly, but still be useful for
    extracting instances for which the relation holds, because its meaning entails
    the meaning of the target relation. We evaluate the usage of both automatically
    generated and gold-standard entailment graphs in a relation extraction scenario
    and present favorable experimental results, exhibiting the benefits of
    structuring and selecting patterns based on entailment graphs.},
    url = {http://www.aclweb.org/anthology/S17-1026}
    }

  • S. Karn and H. Schütze, „End-to-End Trainable Attentive Decoder for Hierarchical Entity Typing,“ in Proceedings of the 2017 Conference of the European Chapter of the Association for Computational Linguistics (EACL, Short Paper), Valencia, Spain, 2017.
    [BibTeX]
    @InProceedings{karn_typing_2017,
    author = {Karn, Sanjeev and Schütze, Hinrich},
    title = {End-to-End Trainable Attentive Decoder for Hierarchical Entity Typing},
    year = {2017},
    booktitle = {Proceedings of the 2017 Conference of the European Chapter of the Association for Computational Linguistics (EACL, Short Paper)},
    address = {Valencia, Spain}
    }

  • T. Rohrmann, S. Schelter, T. Rabl, and V. Markl, „Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems,“ in Proceedings of BTW 2017 (Datenbanksysteme für Business, Technologie und Web), Stuttgart, Germany, 2017.
    [BibTeX]
    @InProceedings{rohrmann:2017,
    author = {Rohrmann, Till and Schelter, Sebastian and Rabl, Tilmann and Markl, Volker},
    title = {Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems},
    booktitle = {Proceedings of BTW 2017 (Datenbanksysteme für Business, Technologie und Web)},
    year = {2017},
    address = {Stuttgart, Germany}
    }

  • R. Schwarzenberg, L. Hennig, and H. Hemsen, „In-Memory Distributed Training of Linear-Chain Conditional Random Fields, with an Application to Fine-Grained Named Entity Recognition,“ in Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology, Berlin, Germany, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{schwarzenberg2017,
    author = {Schwarzenberg, Robert and Hennig, Leonhard and Hemsen, Holmer},
    title = {In-Memory Distributed Training of Linear-Chain Conditional Random Fields, with an Application to Fine-Grained Named Entity Recognition},
    booktitle = {Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology},
    month = {September},
    year = {2017},
    address = {Berlin, Germany},
    _publisher = {German Society for Computational Linguistics and Language Technology},
    _pages = {},
    url = {http://www.dfki.de/lt/publication_show.php?id=9105}
    }

  • R. Schwarzenberg, „Distributed Named Entity Recognition of Fine Grained Geo Locations,“ TU Berlin, Master Thesis , 2017.
    [BibTeX]
    @techreport{schwarzenberg_distributed_2017,
    type = {Master Thesis},
    title = {Distributed {Named} {Entity} {Recognition} of {Fine} {Grained} {Geo} {Locations}},
    institution = {TU Berlin},
    author = {Schwarzenberg, Robert},
    year = {2017}
    }

  • P. Thomas and L. Hennig, „Twitter Geolocation Prediction using Neural Networks,“ in Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology, Berlin, Germany, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{thomas2017b,
    author = {Thomas, Philippe and Hennig, Leonhard},
    title = {Twitter Geolocation Prediction using Neural Networks},
    booktitle = {Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology},
    month = {September},
    year = {2017},
    address = {Berlin, Germany},
    _publisher = {German Society for Computational Linguistics and Language Technology},
    _pages = {},
    url = {http://www.dfki.de/lt/publication_show.php?id=9106}
    }

  • P. Thomas, J. Kirschnick, L. Hennig, R. Ai, S. Schmeier, H. Hemsen, F. Xu, and H. Uszkoreit, „Streaming Text Analytics for Real-time Event Recognition,“ in Proceedings of the International Conference Recent Advances in Natural Language Processing, Varna, Bulgaria, 2017.
    [BibTeX] [Download PDF]
    @InProceedings{thomas2017a,
    author = {Thomas, Philippe and Kirschnick, Johannes and Hennig, Leonhard and Ai, Renlong and Schmeier,Sven and Hemsen, Holmer and Xu, Feiyu and Uszkoreit, Hans},
    title = {Streaming Text Analytics for Real-time Event Recognition},
    booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing},
    month = {September},
    year = {2017},
    address = {Varna, Bulgaria},
    _publisher = {},
    _pages = {},
    url = {http://www.dfki.de/lt/publication_show.php?id=9107}
    }

2016

  • S. Arnold, F. A. Gers, T. Kilias, and A. Löser, „Robust Named Entity Recognition in Idiosyncratic Domains,“ CoRR, vol. abs/1608.06757, 2016.
    [BibTeX] [Download PDF]
    @article{DBLP:journals/corr/ArnoldGKL16,
    author = {Sebastian Arnold and
    Felix A. Gers and
    Torsten Kilias and
    Alexander L{\"{o}}ser},
    title = {Robust Named Entity Recognition in Idiosyncratic Domains},
    journal = {CoRR},
    volume = {abs/1608.06757},
    year = {2016},
    url = {http://arxiv.org/abs/1608.06757},
    timestamp = {Fri, 02 Sep 2016 17:46:24 +0200},
    biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/ArnoldGKL16},
    bibsource = {dblp computer science bibliography, http://dblp.org}
    }

  • S. Arnold, R. Dziuba, and A. Löser, „TASTY: Interactive Entity Linking As-You-Type,“ in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan, 2016, p. 111–115.
    [BibTeX] [Download PDF]
    @InProceedings{arnold-dziuba-loser:2016:COLINGDEMO,
    author = {Arnold, Sebastian and Dziuba, Robert and L\"{o}ser, Alexander},
    title = {TASTY: Interactive Entity Linking As-You-Type},
    booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations},
    month = {December},
    year = {2016},
    address = {Osaka, Japan},
    publisher = {The COLING 2016 Organizing Committee},
    pages = {111--115},
    url = {http://aclweb.org/anthology/C16-2024}
    }

  • C. Baron Neto, K. Müller, M. Brümmer, D. Kontokostas, and S. Hellmann, „LODVader: An Interface to LOD Visualization, Analyticsand DiscovERy in Real-time,“ in Proceedings of the 25th International Conference Companion on World Wide Web, Republic and Canton of Geneva, Switzerland, 2016, p. 163–166. doi:10.1145/2872518.2890545
    [BibTeX] [Download PDF]
    @inproceedings{BaronNeto:2016:LIL:2872518.2890545,
    author = {Baron Neto, Ciro and M\"{u}ller, Kay and Br\"{u}mmer, Martin and Kontokostas, Dimitris and Hellmann, Sebastian},
    title = {LODVader: An Interface to LOD Visualization, Analyticsand DiscovERy in Real-time},
    booktitle = {Proceedings of the 25th International Conference Companion on World Wide Web},
    series = {WWW '16 Companion},
    year = {2016},
    isbn = {978-1-4503-4144-8},
    location = {Montr\&\#233;al, Qu\&\#233;bec, Canada},
    pages = {163--166},
    numpages = {4},
    url = {https://doi.org/10.1145/2872518.2890545},
    doi = {10.1145/2872518.2890545},
    acmid = {2890545},
    publisher = {International World Wide Web Conferences Steering Committee},
    address = {Republic and Canton of Geneva, Switzerland},
    keywords = {bloom filter, linked open data, linksets, rdf diagram},
    }

  • M. Brümmer, M. Dojchinovski, and S. Hellmann, „DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus,“ in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, 2016.
    [BibTeX] [Download PDF]
    @inproceedings{BRMMER16.895,
    added-at = {2017-03-08T16:37:20.000+0100},
    address = {Paris, France},
    author = {Br{\"u}mmer, Martin and Dojchinovski, Milan and Hellmann, Sebastian},
    biburl = {https://www.bibsonomy.org/bibtex/2d73fd2e5802d5bcf946c73897d54783f/aksw},
    booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
    editor = {Chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Declerck, Thierry and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
    interhash = {8efa6a388422eb1170a1abc89f43f2b6},
    intrahash = {d73fd2e5802d5bcf946c73897d54783f},
    keywords = {bruemmer dojchinovski freme group_aksw hellmann kilt},
    month = may,
    publisher = {European Language Resources Association (ELRA)},
    timestamp = {2017-03-08T16:37:20.000+0100},
    title = {DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus},
    url = {https://svn.aksw.org/papers/2016/LREC_DBpedia_Abstracts/public.pdf},
    year = 2016
    }

  • M. Dojchinovski, D. Kontokostas, R. Rößling, M. Knuth, and S. Hellmann, „DBpedia Links: The Hub of Links for the Web of Data,“ in Proceedings of the SEMANTiCS 2016 Conference (SEMANTiCS 2016), 12-15 2016.
    [BibTeX] [Download PDF]
    @inproceedings{DojchinovskiDBpediaLinks,
    added-at = {2017-03-08T16:37:25.000+0100},
    author = {Dojchinovski, Milan and Kontokostas, Dimitris and R{\"o}{\ss}ling, Robert and Knuth, Magnus and Hellmann, Sebastian},
    biburl = {http://www.bibsonomy.org/bibtex/21deda517b750eedad70dd5b10df98bf2/aksw},
    booktitle = {Proceedings of the SEMANTiCS 2016 Conference (SEMANTiCS 2016)},
    date = {12-15},
    interhash = {80c5ed7225e5860f9e301a1cbb00381b},
    intrahash = {1deda517b750eedad70dd5b10df98bf2},
    keywords = {2016 aligned-project dojchinovski freme group_aksw hellmann kilt kontokostas},
    language = {english},
    location = {Leipzig, Germany},
    month = {September},
    timestamp = {2017-03-08T16:37:25.000+0100},
    title = {DBpedia Links: The Hub of Links for the Web of Data},
    url = {https://svn.aksw.org/papers/2016/SEMANTiCS_DBpedia_Links/public.pdf},
    year = 2016
    }

  • M. Freudenberg, M. Brümmer, J. Rücknagel, R. Ulrich, T. Eckart, D. Kontokostas, and S. Hellmann, „The Metadata Ecosystem of DataID,“ in Metadata and Semantics Research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings, E. Garoufallou, I. Subirats Coll, A. Stellato, and J. Greenberg, Eds., Cham: Springer International Publishing, 2016, p. 317–332. doi:10.1007/978-3-319-49157-8_28
    [BibTeX] [Download PDF]
    @Inbook{Freudenberg2016,
    author="Freudenberg, Markus and Br{\"u}mmer, Martin and R{\"u}cknagel, Jessika and Ulrich, Robert and Eckart, Thomas and Kontokostas, Dimitris and Hellmann, Sebastian",
    editor="Garoufallou, Emmanouel and Subirats Coll, Imma and Stellato, Armando and Greenberg, Jane",
    title="The Metadata Ecosystem of DataID",
    bookTitle="Metadata and Semantics Research: 10th International Conference, MTSR 2016, G{\"o}ttingen, Germany, November 22-25, 2016, Proceedings",
    year="2016",
    publisher="Springer International Publishing",
    address="Cham",
    pages="317--332",
    isbn="978-3-319-49157-8",
    doi="10.1007/978-3-319-49157-8_28",
    url="http://dx.doi.org/10.1007/978-3-319-49157-8_28"
    }

  • P. Gupta, H. Schütze, and B. Andrassy, „Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction,“ in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, 2016, p. 2537–2547.
    [BibTeX] [Abstract] [Download PDF]

    This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0% and 2.7% for entity recognition and relation classification, respectively on CoNLL04 dataset.

    @InProceedings{gupta-schutze-andrassy:2016:COLING,
    author = {Gupta, Pankaj and Sch\"{u}tze, Hinrich and Andrassy, Bernt},
    title = {Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction},
    booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
    month = {December},
    year = {2016},
    address = {Osaka, Japan},
    publisher = {The COLING 2016 Organizing Committee},
    pages = {2537--2547},
    abstract = {This paper proposes a novel context-aware joint entity and word-level relation
    extraction approach through semantic composition of words, introducing a Table
    Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the
    entity recognition and relation classification tasks to a table-filling problem
    and models their interdependencies. The proposed neural
    network architecture is capable of modeling multiple relation instances without
    knowing the corresponding relation arguments in a sentence. The experimental
    results show that a simple approach of piggybacking candidate entities to model
    the label dependencies from relations to entities improves performance.
    We present state-of-the-art results with improvements of 2.0% and 2.7% for
    entity recognition and relation classification, respectively on CoNLL04
    dataset.},
    url = {http://aclweb.org/anthology/C16-1239}
    }

  • L. Hennig, P. Thomas, R. Ai, J. Kirschnick, H. Wang, J. Pannier, N. Zimmermann, S. Schmeier, F. Xu, J. Ostwald, and H. Uszkoreit, „Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams,“ in Proceedings of ACL-2016 System Demonstrations, Berlin, Germany, 2016, p. 37–42.
    [BibTeX] [Download PDF]
    @InProceedings{hennig-EtAl:2016:P16-4,
    author = {Hennig, Leonhard and Thomas, Philippe and Ai, Renlong and Kirschnick, Johannes and Wang, He and Pannier, Jakob and Zimmermann, Nora and Schmeier, Sven and Xu, Feiyu and Ostwald, Jan and Uszkoreit, Hans},
    title = {Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams},
    booktitle = {Proceedings of ACL-2016 System Demonstrations},
    month = {August},
    year = {2016},
    address = {Berlin, Germany},
    publisher = {Association for Computational Linguistics},
    pages = {37--42},
    url = {http://www.aclweb.org/anthology/P/P16/P16-4007.pdf}
    }

  • J. Kirschnick, H. Hemsen, and V. Markl, „JEDI: Joint Entity and Relation Detection using Type Inference,“ in Proceedings of ACL-2016 System Demonstrations, Berlin, Germany, 2016, p. 61–66.
    [BibTeX] [Download PDF]
    @InProceedings{kirschnick-hemsen-markl:2016:P16-4,
    author = {Kirschnick, Johannes and Hemsen, Holmer and Markl, Volker},
    title = {JEDI: Joint Entity and Relation Detection using Type Inference},
    booktitle = {Proceedings of ACL-2016 System Demonstrations},
    month = {August},
    year = {2016},
    address = {Berlin, Germany},
    publisher = {Association for Computational Linguistics},
    pages = {61--66},
    url = {http://www.aclweb.org/anthology/P/P16/P16-4011.pdf}
    }

  • S. Krause, F. Xu, H. Uszkoreit, and D. Weissenborn, „Event Linking with Sentential Features from Convolutional Neural Networks,“ in Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, 2016, p. 239–249. doi:10.18653/v1/K16-1024
    [BibTeX] [Download PDF]
    @InProceedings{K16-1024,
    author = "Krause, Sebastian
    and Xu, Feiyu
    and Uszkoreit, Hans
    and Weissenborn, Dirk",
    title = "Event Linking with Sentential Features from Convolutional Neural Networks",
    booktitle = "Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning",
    year = "2016",
    publisher = "Association for Computational Linguistics",
    pages = "239--249",
    location = "Berlin, Germany",
    doi = "10.18653/v1/K16-1024",
    url = "http://www.aclweb.org/anthology/K16-1024"
    }

  • K. Müller, C. Stadler, and R. K. Singh, „Towards Sustainable view-based Extract-Transform-Load (ETL) Fusion of Open Data,“ in Proceedings of the 3rd Workshop on Linked Data Quality co-located with ESWC 2016, 2016.
    [BibTeX]
    @InProceedings{mueller_towards_2016,
    author = {M\"{u}ller, Kay and Stadler, Claus and Singh, Ritesh Kumar},
    title = {Towards Sustainable view-based Extract-Transform-Load (ETL) Fusion of Open Data},
    booktitle = {Proceedings of the 3rd Workshop on Linked Data Quality co-located with ESWC 2016},
    year = {2016}
    }

  • C. B. Neto, D. Kontokostas, S. Hellmann, K. Müller, and M. Brümmer, „Assessing Quantity and Quality of Links Between Linked Data Datasets,“ in Proceedings of the Workshop on Linked Data on the Web co-located with the 25th International World Wide Web Conference (WWW 2016), 2016.
    [BibTeX]
    @inproceedings{baron-2016-ldow-assessing-links,
    added-at = {2017-03-08T16:37:51.000+0100},
    author = {Neto, Ciro Baron and Kontokostas, Dimitris and Hellmann, Sebastian and M{\"u}ller, Kay and Br{\"u}mmer, Martin},
    biburl = {https://www.bibsonomy.org/bibtex/206325f941b000843d843367e0277f943/aksw},
    booktitle = {Proceedings of the Workshop on Linked Data on the Web co-located with the 25th International World Wide Web Conference (WWW 2016)},
    interhash = {f49919d9c98f3a3bf7cdb463a99b52a9},
    intrahash = {06325f941b000843d843367e0277f943},
    keywords = {baron bruemmer group_aksw hellmann kilt kmueller kontokostas},
    month = apr,
    owner = {baron},
    timestamp = {2017-03-08T16:37:51.000+0100},
    title = {Assessing Quantity and Quality of Links Between Linked Data Datasets},
    year = 2016
    }

  • C. B. Neto, D. Kontokostas, G. Publio, K. Müller, S. Hellmann, and E. Moletta, „LD-LEx: Linked Dataset Link Extractor (Short Paper),“ in On the Move to Meaningful Internet Systems: OTM 2016 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, Rhodes, Greece, October 24-28, 2016, Proceedings, C. Debruyne, H. Panetto, R. Meersman, T. Dillon, E. Kühn, D. O’Sullivan, and C. A. Ardagna, Eds., Cham: Springer International Publishing, 2016, p. 727–734. doi:10.1007/978-3-319-48472-3_45
    [BibTeX] [Download PDF]
    @Inbook{Neto2016,
    author="Neto, Ciro Baron and Kontokostas, Dimitris and Publio, Gustavo and M{\"u}ller, Kay and Hellmann, Sebastian and Moletta, Eduardo",
    editor="Debruyne, Christophe and Panetto, Herv{\'e} and Meersman, Robert and Dillon, Tharam and K{\"u}hn, Eva and O'Sullivan, Declan and Ardagna, Claudio Agostino",
    title="LD-LEx: Linked Dataset Link Extractor (Short Paper)",
    bookTitle="On the Move to Meaningful Internet Systems: OTM 2016 Conferences: Confederated International Conferences: CoopIS, C{\&}TC, and ODBASE 2016, Rhodes, Greece, October 24-28, 2016, Proceedings",
    year="2016",
    publisher="Springer International Publishing",
    address="Cham",
    pages="727--734",
    isbn="978-3-319-48472-3",
    doi="10.1007/978-3-319-48472-3_45",
    url="http://dx.doi.org/10.1007/978-3-319-48472-3_45"
    }

  • R. Schneider, C. Guder, T. Kilias, A. Löser, J. Graupmann, and O. Kozachuk, „Interactive Relation Extraction in Main Memory Database Systems,“ in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan, 2016, p. 103–106.
    [BibTeX] [Abstract] [Download PDF]

    We present INDREX-MM, a main memory database system for interactively executing two interwoven tasks, declarative relation extraction from text and their exploitation with SQL. INDREX-MM simplifies these tasks for the user with powerful SQL extensions for gathering statistical semantics, for executing open information extraction and for integrating relation candidates with domain specific data. We demonstrate these functions on 800k documents from Reuters RCV1 with more than a billion linguistic annotations and report execution times in the order of seconds.

    @InProceedings{schneider-EtAl:2016:COLINGDEMO,
    author = {Schneider, Rudolf and Guder, Cordula and Kilias, Torsten and L\"{o}ser, Alexander and Graupmann, Jens and Kozachuk, Oleksandr},
    title = {Interactive Relation Extraction in Main Memory Database Systems},
    booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations},
    month = {December},
    year = {2016},
    address = {Osaka, Japan},
    publisher = {The COLING 2016 Organizing Committee},
    pages = {103--106},
    abstract = {We present INDREX-MM, a main memory database system for interactively executing
    two interwoven tasks, declarative relation extraction from text and their
    exploitation with SQL. INDREX-MM simplifies these tasks for the user with
    powerful SQL extensions for gathering statistical semantics, for executing open
    information extraction and for integrating relation candidates with domain
    specific data. We demonstrate these functions on 800k documents from Reuters
    RCV1 with more than a billion linguistic annotations and report execution times
    in the order of seconds.},
    url = {http://aclweb.org/anthology/C16-2022}
    }

  • N. T. Vu, H. Adel, P. Gupta, and H. Schütze, „Combining Recurrent and Convolutional Neural Networks for Relation Classification,“ in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, 2016, p. 534–539.
    [BibTeX] [Download PDF]
    @InProceedings{vu-EtAl:2016:N16-1,
    author = {Vu, Ngoc Thang and Adel, Heike and Gupta, Pankaj and Sch\"{u}tze, Hinrich},
    title = {Combining Recurrent and Convolutional Neural Networks for Relation Classification},
    booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
    month = {June},
    year = {2016},
    address = {San Diego, California},
    publisher = {Association for Computational Linguistics},
    pages = {534--539},
    url = {http://www.aclweb.org/anthology/N16-1065}
    }

2015

  • S. Arnold, A. Löser, and T. Kilias, „Resolving Common Analytical Tasks in Text Databases,“ in Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP, New York, NY, USA, 2015, p. 75–84. doi:10.1145/2811222.2811224
    [BibTeX] [Download PDF]
    @inproceedings{arnold_dolap_2015,
    author = {Arnold, Sebastian and L\"{o}ser, Alexander and Kilias, Torsten},
    title = {Resolving Common Analytical Tasks in Text Databases},
    booktitle = {Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP},
    series = {DOLAP '15},
    year = {2015},
    isbn = {978-1-4503-3785-4},
    location = {Melbourne, Australia},
    pages = {75--84},
    numpages = {10},
    url = {http://doi.acm.org/10.1145/2811222.2811224},
    doi = {10.1145/2811222.2811224},
    acmid = {2811224},
    publisher = {ACM},
    address = {New York, NY, USA},
    keywords = {informational search, keywords, query intent, user goals},
    }

  • L. Hennig, H. Li, S. Krause, F. Xu, and H. Uszkoreit, „A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns,“ in Proceedings of ACL-IJCNLP 2015 System Demonstrations, Beijing, China, 2015, p. 43–48.
    [BibTeX] [Download PDF]
    @InProceedings{hennig_acl_2015,
    author = {Hennig, Leonhard and Li, Hong and Krause, Sebastian and Xu, Feiyu and Uszkoreit, Hans},
    title = {A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns},
    booktitle = {Proceedings of ACL-IJCNLP 2015 System Demonstrations},
    month = {July},
    year = {2015},
    address = {Beijing, China},
    publisher = {Association for Computational Linguistics and The Asian Federation of Natural Language Processing},
    pages = {43--48},
    url = {http://www.aclweb.org/anthology/P15-4008}
    }

  • T. Kilias, A. Löser, and P. Andritsos, „INDREX: In-database relation extraction,“ Information Systems, vol. 53, pp. 124-144, 2015. doi:http://dx.doi.org/10.1016/j.is.2014.11.006
    [BibTeX] [Download PDF]
    @article{kilias_is_2015,
    title = "INDREX: In-database relation extraction ",
    journal = "Information Systems ",
    volume = "53",
    number = "",
    pages = "124 - 144",
    year = "2015",
    issn = "0306-4379",
    doi = "http://dx.doi.org/10.1016/j.is.2014.11.006",
    url = "http://www.sciencedirect.com/science/article/pii/S0306437914001823",
    author = "Torsten Kilias and Alexander Löser and Periklis Andritsos",
    }

  • S. Krause, L. Hennig, A. Gabryszak, F. Xu, and H. Uszkoreit, „Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language,“ in Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications (ACL 2015), Beijing, China, 2015, p. 30–38.
    [BibTeX] [Download PDF]
    @InProceedings{krause_acl_2015,
    author = {Krause, Sebastian and Hennig, Leonhard and Gabryszak, Aleksandra and Xu, Feiyu and Uszkoreit, Hans},
    title = {Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language},
    booktitle = {Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications (ACL 2015)},
    month = {July},
    year = {2015},
    address = {Beijing, China},
    publisher = {Association for Computational Linguistics},
    pages = {30--38},
    url = {http://www.aclweb.org/anthology/W15-4204}
    }