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Integrating deep linguistics features in factuality prediction over unified datasets
Stanovsky, Gabriel; Eckle-Kohler, Judith; Puzikov, Yevgeniy; Dagan, Ido; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017
37874 Endnote
Autor*innen:
Stanovsky, Gabriel; Eckle-Kohler, Judith; Puzikov, Yevgeniy; Dagan, Ido; Gurevych, Iryna
Titel:
Integrating deep linguistics features in factuality prediction over unified datasets
Aus:
Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 352-357
DOI:
10.18653/v1/P17-2056
URL:
https://aclanthology.info/pdf/P/P17/P17-2056.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Modell; Methode; Daten; Sprache; Wort; Semantik
Abstract:
Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Grasping the materializations of practices in Digital Humanities. A semantic research environment […]
Hocker, Julian; Schindler, Christoph; Müller, Lars; Mealeshkova, Maria; Weller, Tobias
Sammelbandbeitrag
| Aus: Gäde, Maria;Trkulja, Violeta;Petras, Vivien (Hrsg.): Everything changes, everything stays the same? Understanding information spaces: Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin, Germany, 13th - 15th March 2017 | Glückstadt: Hülsbusch | 2017
37199 Endnote
Autor*innen:
Hocker, Julian; Schindler, Christoph; Müller, Lars; Mealeshkova, Maria; Weller, Tobias
Titel:
Grasping the materializations of practices in Digital Humanities. A semantic research environment for analyzing exam grading practices in German high schools
Aus:
Gäde, Maria;Trkulja, Violeta;Petras, Vivien (Hrsg.): Everything changes, everything stays the same? Understanding information spaces: Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin, Germany, 13th - 15th March 2017, Glückstadt: Hülsbusch, 2017 (Schriften zur Informationswissenschaft, 70), S. 365-367
URL:
http://isi2017.ib.hu-berlin.de/ISI_17_ONLINE_FINAL.pdf#page=366
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Semantik; Computerunterstütztes Verfahren; Digitalisierung; Geisteswissenschaften; Forschungsprojekt; Forschungsdesign; Abschlussprüfung; Sekundarbereich; Notengebung; Textanalyse
DIPF-Abteilung:
Bibliothek für Bildungsgeschichtliche Forschung; Informationszentrum Bildung
Generating training data for semantic role labeling based on label transfer from linked lexical […]
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Zeitschriftenbeitrag
| In: Transactions of the Association for Computational Linguistics | 2016
36232 Endnote
Autor*innen:
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Titel:
Generating training data for semantic role labeling based on label transfer from linked lexical resources
In:
Transactions of the Association for Computational Linguistics, (2016)
URL:
https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/717
Dokumenttyp:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache:
Englisch
Schlagwörter:
Ambiguität; Automatisierung; Computerlinguistik; Computerunterstütztes Verfahren; Semantik; Textanalyse; Wort; Wörterbuch
Abstract (english):
We present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., WordNet, FrameNet, Wiktionary) by linking them on the sense or on the role level. Unlike resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses and roles. The automatically labeled training data (http://www.ukp.tu-darmstadt.de/knowledge-based-srl/) are evaluated on four corpora from different domains for the tasks of word sense disambiguation and semantic role classification. Results show that classifiers trained on our generated data equal those resulting from a standard supervised setting. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Supersense embeddings. A unified model for supersense interpretation, prediction and utilization
Flekova, Lucie; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016): Long Papers | Stroudsburg; PA: Association for Computational Linguistics | 2016
36971 Endnote
Autor*innen:
Flekova, Lucie; Gurevych, Iryna
Titel:
Supersense embeddings. A unified model for supersense interpretation, prediction and utilization
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016): Long Papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 2029-2041
URL:
http://www.aclweb.org/anthology/P16-1191
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Beziehung; Computerlinguistik; Klassifikation; Semantik; Sinn; Wort
Abstract (english):
Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Modeling extractive sentence intersection via subtree entailment
Levy, Omer; Dagan, Ido; Stanovsky, Gabriel; Eckle-Kohler, Judith; Gurevych, Iryna
Sammelbandbeitrag
| Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) | Osaka: The COLING 2016 Organizing Committee | 2016
36987 Endnote
Autor*innen:
Levy, Omer; Dagan, Ido; Stanovsky, Gabriel; Eckle-Kohler, Judith; Gurevych, Iryna
Titel:
Modeling extractive sentence intersection via subtree entailment
Aus:
The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 2891-2901
URL:
http://www.aclweb.org/anthology/C/C16/C16-1272.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Computerlinguistik; Daten; Klassifikation; Semantik; Struktur; Syntax; Text
Abstract (english):
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. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Task-oriented intrinsic evaluation of semantic textual similarity
Reimers, Nils; Beyer, Philip; Gurevych, Iryna
Sammelbandbeitrag
| Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) | Osaka: The COLING 2016 Organizing Committee | 2016
36988 Endnote
Autor*innen:
Reimers, Nils; Beyer, Philip; Gurevych, Iryna
Titel:
Task-oriented intrinsic evaluation of semantic textual similarity
Aus:
The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 87-96
URL:
https://www.aclweb.org/anthology/C/C16/C16-1009.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Evaluation; Korrelation; Messverfahren; Semantik; Systemvergleich; Text
Abstract (english):
Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS systems exist, however, for an NLP system designer, it is hard to decide which system is the best on. To answer this question, an intrinsic evaluation of the STS systems is conducted by comparing the output of the system to human judgments on semantic similarity. The comparison is usually done using Pearson cor- relation. In this work, we show that relying on intrinsic evaluations with Pearson correlation can be misleading. In three common STS based tasks we could observe that the Pearson correlation was especially ill-suited to detect the best STS system for the task and other evaluation measures were much better suited. In this work we define how the validity of an intrinsic evaluation can be assessed and compare different intrinsic evaluation methods. Understanding of the properties of the targeted task is crucial and we propose a framework for conducting the intrinsic evaluation which takes the properties of the targeted task into account. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Token-level metaphor detection using neural networks
Do Dinh, Erik-Lân; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the fourth workshop on metaphor in NLP held in conjunction with NAACL 2016 | Stroudsburg; PA: Association for Computational Linguistics | 2016
36978 Endnote
Autor*innen:
Do Dinh, Erik-Lân; Gurevych, Iryna
Titel:
Token-level metaphor detection using neural networks
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the fourth workshop on metaphor in NLP held in conjunction with NAACL 2016, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 28-33
URL:
https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/2016_DoDinh_NAACL_pages.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Netzwerk; Semantik; Textanalyse
Abstract (english):
Automatic metaphor detection usually relies on various features, incorporating e.g. selectional preference violations or concreteness ratings to detect metaphors in text. These features rely on background corpora, hand-coded rules or additional, manually created resources, all specific to the language the system is being used on. We present a novel approach to metaphor detection using a neural network in combination with word embeddings, a method that has already proven to yield promising results for other natural language processing tasks. We show that foregoing manual feature engineering by solely relying on word embeddings trained on large corpora produces comparable results to other systems, while removing the need for additional resources. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Medical concept embeddings via labeled background corpora
Mencía, Eneldo Loza; De Melo, Gerard; Nam, Jinseok
Sammelbandbeitrag
| Aus: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | Portoroz: European Language Resources Association | 2016
37067 Endnote
Autor*innen:
Mencía, Eneldo Loza; De Melo, Gerard; Nam, Jinseok
Titel:
Medical concept embeddings via labeled background corpora
Aus:
European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), Portoroz: European Language Resources Association, 2016 , S. 3629-3636
URL:
http://www.lrec-conf.org/proceedings/lrec2016/pdf/1190_Paper.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Automatisierung; Computerlinguistik; Medizin; Semantik; Sprache; Textanalyse
Abstract:
In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Enriching wikidata with frame semantics
Mousselly-Sergieh, Hatem; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 5th workshop on automated knowledge base construction (AKBC) 2016 held in conjunction with NAACL 2016 | Stroudsburg; PA: Association for Computational Linguistics | 2016
36979 Endnote
Autor*innen:
Mousselly-Sergieh, Hatem; Gurevych, Iryna
Titel:
Enriching wikidata with frame semantics
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 5th workshop on automated knowledge base construction (AKBC) 2016 held in conjunction with NAACL 2016, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 29-34
URL:
https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/2016_NAACL_AKBC_HMS.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Lexikon; Mehrsprachigkeit; Online; Semantik
Abstract (english):
Wikidata is a large-scale, multilingual and freely available knowledge base. It contains more than 14 million facts, however, it is still missing linguistic information. In this paper, we aim to bridge this gap by aligning Wikidata with FrameNet lexicon. We propose an approach based on word embedding to identify a mapping between Wikidata relations, called properties, and FrameNet frames and to annotate the arguments of each relation with the semantic roles of the matching frames. Early empirical results show the advantage of our approach compared to other baseline methods. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations
Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna
Sammelbandbeitrag
| Aus: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | Portoroz: European Language Resources Association | 2016
36972 Endnote
Autor*innen:
Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna
Titel:
Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations
Aus:
European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), Portoroz: European Language Resources Association, 2016 , S. 2131-2137
URL:
https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/lrec2016_sukhareva.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Semantik; Textanalyse
Abstract (english):
We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
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