Search Results for author: Alan Akbik

Found 30 papers, 6 papers with code

Task-Specific Embeddings for Ante-Hoc Explainable Text Classification

no code implementations30 Nov 2022 Kishaloy Halder, Josip Krapac, Alan Akbik, Anthony Brew, Matti Lyra

In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions.

Incremental Learning text-classification +1

Early Detection of Sexual Predators in Chats

1 code implementation ACL 2021 Matthias Vogt, Ulf Leser, Alan Akbik

We define and study the task of early sexual predator detection (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible.

Early Sexual Predator Detection (eSPD)

Task-Aware Representation of Sentences for Generic Text Classification

1 code implementation COLING 2020 Kishaloy Halder, Alan Akbik, Josip Krapac, Roland Vollgraf

State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem.

text-classification Text Classification +1

FLERT: Document-Level Features for Named Entity Recognition

1 code implementation13 Nov 2020 Stefan Schweter, Alan Akbik

Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries.

named-entity-recognition Named Entity Recognition +1

FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP

1 code implementation NAACL 2019 Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Rol Vollgraf,

We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.

Chunking Named Entity Recognition (NER) +2

Contextual String Embeddings for Sequence Labeling

1 code implementation COLING 2018 Alan Akbik, Duncan Blythe, Rol Vollgraf,

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.

Chunking Language Modelling +4

Syntax-Aware Language Modeling with Recurrent Neural Networks

no code implementations2 Mar 2018 Duncan Blythe, Alan Akbik, Roland Vollgraf

Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words.

Language Modelling

The Projector: An Interactive Annotation Projection Visualization Tool

no code implementations EMNLP 2017 Alan Akbik, Rol Vollgraf,

Previous works proposed annotation projection in parallel corpora to inexpensively generate treebanks or propbanks for new languages.

K-SRL: Instance-based Learning for Semantic Role Labeling

no code implementations COLING 2016 Alan Akbik, Yunyao Li

To overcome this challenge, we propose the use of instance-based learning that performs no explicit generalization, but rather extrapolates predictions from the most similar instances in the training data.

Machine Translation Question Answering +1

Multilingual Information Extraction with PolyglotIE

no code implementations COLING 2016 Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yonas Kbrom, Yunyao Li, Huaiyu Zhu

We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data.

Semantic Parsing

Freepal: A Large Collection of Deep Lexico-Syntactic Patterns for Relation Extraction

no code implementations LREC 2014 Johannes Kirschnick, Alan Akbik, Holmer Hemsen

The increasing availability and maturity of both scalable computing architectures and deep syntactic parsers is opening up new possibilities for Relation Extraction (RE) on large corpora of natural language text.

Entity Linking Relation Extraction

The Weltmodell: A Data-Driven Commonsense Knowledge Base

no code implementations LREC 2014 Alan Akbik, Thilo Michael

We present the Weltmodell, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3. 5 million English language books.

Association Open Information Extraction

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