Search Results for author: Yadollah Yaghoobzadeh

Found 18 papers, 4 papers with code

Quantifying the Contextualization of Word Representations with Semantic Class Probing

no code implementations Findings of the Association for Computational Linguistics 2020 Mengjie Zhao, Philipp Dufter, Yadollah Yaghoobzadeh, Hinrich Schütze

Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.

Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing

1 code implementation EMNLP 2018 Yadollah Yaghoobzadeh, Hinrich Schütze

For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.

Entity Typing Multiview Learning +1

Corpus-level Fine-grained Entity Typing

no code implementations7 Aug 2017 Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze

This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".

Entity Typing Knowledge Base Completion

Noise Mitigation for Neural Entity Typing and Relation Extraction

no code implementations EACL 2017 Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze

For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction.

Entity Typing Multi-Label Learning +1

Corpus-level Fine-grained Entity Typing Using Contextual Information

no code implementations EMNLP 2015 Yadollah Yaghoobzadeh, Hinrich Schütze

This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".

Entity Typing Knowledge Base Completion +1

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