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Alan Akbik
Researcher at IBM
Publications - 39
Citations - 2523
Alan Akbik is an academic researcher from IBM. The author has contributed to research in topics: Information extraction & Relationship extraction. The author has an hindex of 13, co-authored 36 publications receiving 1763 citations. Previous affiliations of Alan Akbik include Humboldt University of Berlin & Technical University of Berlin.
Papers
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Proceedings Article
Contextual String Embeddings for Sequence Labeling
TL;DR: This paper proposes to leverage the internal states of a trained character language model to produce a novel type of word embedding which they refer to as contextual string embeddings, which are fundamentally model words as sequences of characters and are contextualized by their surrounding text.
Proceedings ArticleDOI
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
TL;DR: The core idea of the FLAIR framework is to present a simple, unified interface for conceptually very different types of word and document embeddings, which effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” variousembeddings with little effort.
Proceedings ArticleDOI
Pooled Contextualized Embeddings for Named Entity Recognition.
TL;DR: This work proposes a method in which it dynamically aggregate contextualized embeddings of each unique string that the authors encounter and uses a pooling operation to distill a ”global” word representation from all contextualized instances.
Proceedings ArticleDOI
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling
TL;DR: This paper presents a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data and shows that this method outperforms existing methods.
Proceedings Article
KrakeN: N-ary Facts in Open Information Extraction
Alan Akbik,Alexander Löser +1 more
TL;DR: KrakeN is an OIE system specifically designed to capture N-ary facts, as well as the results of an experimental study on extracting facts from Web text in which the issue of fact completeness is examined.