Sammelbandbeitrag - Artikel | 2017 | Not reviewed

Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models

Autor(en)

ter Horst, Hendrik ; Hartung, Matthias ; Cimiano, Philipp

Herausgeber*innen

Gracia, Jorge ; Bond, Francis ; McCrae, John P. ; Buitelaar, Paul ; Chiarcos, Christian ; Hellmann, Sebastian

Titel des Sammelbandes

Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference)

Verlag

Springer

Name der Konferenz

1st International Conference on Language, Data, and Knowledge (LDK)

URN

urn:nbn:de:0070-pub-29103365

URL

https://pub.uni-bielefeld.de/record/2910336

Weitere Details

Sprache

englisch

Darstellungsform

Text

Serie

Lecture Notes in Artificial Intelligence

Band

10318

Seitenbereich

166 - 180

Schlagwörter

Chemicals , Diseases , Joint entity recognition and linking , Undirected probabilistic graphical models

Link zum Volltext (intern)

https://pub.uni-bielefeld.de/record/2910336

DDC

000

The problems of recognizing mentions of entities in texts and linking them to unique knowledge base identifiers have received considerable attention in recent years. In this paper we present a probabilistic system based on undirected graphical models that jointly addresses both the entity recognition and the linking task. Our framework considers the span of mentions of entities as well as the corresponding knowledge base identifier as random variables and models the joint assignment using a factorized distribution. We show that our approach can be easily applied to different technical domains by merely exchanging the underlying ontology. On the task of recognizing and linking disease names, we show that our approach outperforms the state-of-the-art systems DNorm and TaggerOne, as well as two strong lexicon-based baselines. On the task of recognizing and linking chemical names, our system achieves comparable performance to the state-of-the-art.

Zitierstile

ter Horst H, Hartung M, Cimiano P. Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models. In: Gracia J, Bond F, McCrae JP, Buitelaar P, Chiarcos C, Hellmann S, eds. Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference). Lecture Notes in Artificial Intelligence. Vol 10318. Springer; 2017: 166-180.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., & Cimiano, P. (2017). Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models. In J. Gracia, F. Bond, J. P. McCrae, P. Buitelaar, C. Chiarcos, & S. Hellmann (Eds.), Lecture Notes in Artificial Intelligence: Vol. 10318. Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference) (pp. 166-180). Springer.

In die Zwischenablage einfügen

ter Horst, Hendrik, Hartung, Matthias, and Cimiano, Philipp. 2017. “Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models”. In Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference), ed. Jorge Gracia, Francis Bond, John P. McCrae, Paul Buitelaar, Christian Chiarcos, and Sebastian Hellmann, 10318:166-180. Lecture Notes in Artificial Intelligence. Springer.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., and Cimiano, P. (2017). “Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models” in Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference), Gracia, J., Bond, F., McCrae, J. P., Buitelaar, P., Chiarcos, C., and Hellmann, S. eds. Lecture Notes in Artificial Intelligence, vol. 10318, (Springer), 166-180.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., & Cimiano, P., 2017. Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models. In J. Gracia, et al., eds. Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference). Lecture Notes in Artificial Intelligence. no.10318 Springer, pp. 166-180.

In die Zwischenablage einfügen

H. ter Horst, M. Hartung, and P. Cimiano, “Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models”, Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference), J. Gracia, et al., eds., Lecture Notes in Artificial Intelligence, vol. 10318, Springer, 2017, pp.166-180.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., Cimiano, P.: Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models. In: Gracia, J., Bond, F., McCrae, J.P., Buitelaar, P., Chiarcos, C., and Hellmann, S. (eds.) Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference). Lecture Notes in Artificial Intelligence. 10318, p. 166-180. Springer (2017).

In die Zwischenablage einfügen

ter Horst, Hendrik, Hartung, Matthias, and Cimiano, Philipp. “Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models”. Language, Data, and Knowledge (Proceedings of the 1st International LDK Conference). Ed. Jorge Gracia, Francis Bond, John P. McCrae, Paul Buitelaar, Christian Chiarcos, and Sebastian Hellmann. Springer, 2017.Vol. 10318. Lecture Notes in Artificial Intelligence. 166-180.

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