Steffen Remus


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Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva | Denis Teslenko | Artem Shelmanov | Steffen Remus | Dmitry Ustalov | Andrey Kutuzov | Ekaterina Artemova | Chris Biemann | Simone Paolo Ponzetto | Alexander Panchenko
Proceedings of the 12th Language Resources and Evaluation Conference

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al., (2018), enabling WSD in these languages. Models and system are available online.


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Hierarchical Multi-label Classification of Text with Capsule Networks
Rami Aly | Steffen Remus | Chris Biemann
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs. For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC). Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.

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LT Expertfinder: An Evaluation Framework for Expert Finding Methods
Tim Fischer | Steffen Remus | Chris Biemann
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Expert finding is the task of ranking persons for a predefined topic or search query. Finding experts for a specified area is an important task and has attracted much attention in the information retrieval community. Most approaches for this task are evaluated in a supervised fashion, which depend on predefined topics of interest as well as gold standard expert rankings. Famous representatives of such datasets are enriched versions of DBLP provided by the ArnetMiner projet or the W3C Corpus of TREC. However, manually ranking experts can be considered highly subjective and detailed rankings are hardly distinguishable. Evaluating these datasets does not necessarily guarantee a good or bad performance of the system. Particularly for dynamic systems, where topics are not predefined but formulated as a search query, we believe a more informative approach is to perform user studies for directly comparing different methods in the same view. In order to accomplish this in a user-friendly way, we present the LT Expert Finder web-application, which is equipped with various query-based expert finding methods that can be easily extended, a detailed expert profile view, detailed evidence in form of relevant documents and statistics, and an evaluation component that allows the qualitative comparison between different rankings.


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Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities
Steffen Remus | Chris Biemann
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
Seid Muhie Yimam | Steffen Remus | Alexander Panchenko | Andreas Holzinger | Chris Biemann
Proceedings of the Biomedical NLP Workshop associated with RANLP 2017

In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.


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EmpiriST: AIPHES - Robust Tokenization and POS-Tagging for Different Genres
Steffen Remus | Gerold Hintz | Chris Biemann | Christian M. Meyer | Darina Benikova | Judith Eckle-Kohler | Margot Mieskes | Thomas Arnold
Proceedings of the 10th Web as Corpus Workshop

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TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling
Alexander Panchenko | Stefano Faralli | Eugen Ruppert | Steffen Remus | Hubert Naets | Cédrick Fairon | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Domain-Specific Corpus Expansion with Focused Webcrawling
Steffen Remus | Chris Biemann
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software.


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Do Supervised Distributional Methods Really Learn Lexical Inference Relations?
Omer Levy | Steffen Remus | Chris Biemann | Ido Dagan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Unsupervised Relation Extraction of In-Domain Data from Focused Crawls
Steffen Remus
Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics


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Three Knowledge-Free Methods for Automatic Lexical Chain Extraction
Steffen Remus | Chris Biemann
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies