Alexander Panchenko


2020

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Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
Dmitry Ustalov | Swapna Somasundaran | Alexander Panchenko | Fragkiskos D. Malliaros | Ioana Hulpuș | Peter Jansen | Abhik Jana
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

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SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection
Daryna Dementieva | Igor Markov | Alexander Panchenko
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents a solution for the Span Identification (SI) task in the “Detection of Propaganda Techniques in News Articles” competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.

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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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Generating Lexical Representations of Frames using Lexical Substitution
Saba Anwar | Artem Shelmanov | Alexander Panchenko | Chris Biemann
Proceedings of the Probability and Meaning Conference (PaM 2020)

Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goods, and lexical units (LUs) – words and phrases that can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hinders wider adoption of frame semantics across languages and domains. We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized models demonstrate overall superior performance compared to the non-contextualized ones on roles. However, the latter show comparable performance on the task of LU expansion.

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Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Nikolay Arefyev | Boris Sheludko | Alexander Podolskiy | Alexander Panchenko
Proceedings of the 28th International Conference on Computational Linguistics

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.

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Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina | Varvara Logacheva | Alexander Panchenko | Natalia Loukachevitch
Proceedings of the 28th International Conference on Computational Linguistics

Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.

2019

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On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
Abhik Jana | Dima Puzyrev | Alexander Panchenko | Pawan Goyal | Chris Biemann | Animesh Mukherjee
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.

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Making Fast Graph-based Algorithms with Graph Metric Embeddings
Andrey Kutuzov | Mohammad Dorgham | Oleksiy Oliynyk | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.

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Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings
Rami Aly | Shantanu Acharya | Alexander Ossa | Arne Köhn | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce the use of Poincaré embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hyponym terms within a (pre-induced) taxonomy as well as for attaching disconnected terms in a taxonomy. This method substantially improves previous state-of-the-art results on the SemEval-2016 Task 13 on taxonomy extraction. We demonstrate the superiority of Poincaré embeddings over distributional semantic representations, supporting the hypothesis that they can better capture hierarchical lexical-semantic relationships than embeddings in the Euclidean space.

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Improving Neural Entity Disambiguation with Graph Embeddings
Özge Sevgili | Alexander Panchenko | Chris Biemann
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.

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TARGER: Neural Argument Mining at Your Fingertips
Artem Chernodub | Oleksiy Oliynyk | Philipp Heidenreich | Alexander Bondarenko | Matthias Hagen | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user’s side. The open source code ensures portability to other domains and use cases.

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A Dataset for Noun Compositionality Detection for a Slavic Language
Dmitry Puzyrev | Artem Shelmanov | Alexander Panchenko | Ekaterina Artemova
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds. The compound phrases are collected from the Universal Dependency treebanks according to part of speech patterns, such as ADJ+NOUN or NOUN+NOUN, using the gold-standard annotations. Each compound phrase is annotated by two experts and a moderator according to the following schema: the phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). We conduct an experimental evaluation of models and methods for predicting compositionality of noun compounds in unsupervised and supervised setups. We show that methods from previous work evaluated on the proposed Russian-language resource achieve the performance comparable with results on English corpora.

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Categorizing Comparative Sentences
Alexander Panchenko | Alexander Bondarenko | Mirco Franzek | Matthias Hagen | Chris Biemann
Proceedings of the 6th Workshop on Argument Mining

We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., “Python has better NLP libraries than MATLAB” → Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of “better” or “worse”). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.

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Combining Lexical Substitutes in Neural Word Sense Induction
Nikolay Arefyev | Boris Sheludko | Alexander Panchenko
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.

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Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
Dmitry Ustalov | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Computational Linguistics, Volume 45, Issue 3 - September 2019

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.

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Learning Graph Embeddings from WordNet-based Similarity Measures
Andrey Kutuzov | Mohammad Dorgham | Oleksiy Oliynyk | Chris Biemann | Alexander Panchenko
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.

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Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction
Nikolay Arefyev | Boris Sheludko | Adis Davletov | Dmitry Kharchev | Alex Nevidomsky | Alexander Panchenko
Proceedings of the 13th International Workshop on Semantic Evaluation

We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering.

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HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Saba Anwar | Dmitry Ustalov | Nikolay Arefyev | Simone Paolo Ponzetto | Chris Biemann | Alexander Panchenko
Proceedings of the 13th International Workshop on Semantic Evaluation

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.

2018

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Enriching Frame Representations with Distributionally Induced Senses
Stefano Faralli | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
Dmitry Ustalov | Denis Teslenko | Alexander Panchenko | Mikhail Chernoskutov | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Improving Hypernymy Extraction with Distributional Semantic Classes
Alexander Panchenko | Dmitry Ustalov | Stefano Faralli | Simone P. Ponzetto | Chris Biemann
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
Alexander Panchenko | Eugen Ruppert | Stefano Faralli | Simone P. Ponzetto | Chris Biemann
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Unsupervised Semantic Frame Induction using Triclustering
Dmitry Ustalov | Alexander Panchenko | Andrey Kutuzov | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.

2017

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Watset: Automatic Induction of Synsets from a Graph of Synonyms
Dmitry Ustalov | Alexander Panchenko | Chris Biemann
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.

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Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
Alexander Panchenko | Eugen Ruppert | Stefano Faralli | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

The current trend in NLP is the use of highly opaque models, e.g. neural networks and word embeddings. While these models yield state-of-the-art results on a range of tasks, their drawback is poor interpretability. On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy. Namely, we present an unsupervised, knowledge-free WSID approach, which is interpretable at three levels: word sense inventory, sense feature representations, and disambiguation procedure. Experiments show that our model performs on par with state-of-the-art word sense embeddings and other unsupervised systems while offering the possibility to justify its decisions in human-readable form.

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The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links
Stefano Faralli | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.

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Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
Dmitry Ustalov | Nikolay Arefyev | Chris Biemann | Alexander Panchenko
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.

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Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation
Alexander Panchenko | Stefano Faralli | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.

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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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Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Alexander Panchenko | Fide Marten | Eugen Ruppert | Stefano Faralli | Dmitry Ustalov | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.

2016

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Best of Both Worlds: Making Word Sense Embeddings Interpretable
Alexander Panchenko
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.

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new/s/leak – Information Extraction and Visualization for Investigative Data Journalists
Seid Muhie Yimam | Heiner Ulrich | Tatiana von Landesberger | Marcel Rosenbach | Michaela Regneri | Alexander Panchenko | Franziska Lehmann | Uli Fahrer | Chris Biemann | Kathrin Ballweg
Proceedings of ACL-2016 System Demonstrations

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Making Sense of Word Embeddings
Maria Pelevina | Nikolay Arefiev | Chris Biemann | Alexander Panchenko
Proceedings of the 1st Workshop on Representation Learning for NLP

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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)

2013

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A Graph-Based Approach to Skill Extraction from Text
Ilkka Kivimäki | Alexander Panchenko | Adrien Dessy | Dries Verdegem | Pascal Francq | Hugues Bersini | Marco Saerens
Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing

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Search and Visualization of Semantically Related Words (Recherche et visualisation de mots sémantiquement liés) [in French]
Alexander Panchenko | Hubert Naets | Laetitia Brouwers | Pavel Romanov | Cédrick Fairon
Proceedings of TALN 2013 (Volume 2: Short Papers)

2012

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A Study of Heterogeneous Similarity Measures for Semantic Relation Extraction
Alexander Panchenko
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 3: RECITAL

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A Study of Hybrid Similarity Measures for Semantic Relation Extraction
Alexander Panchenko | Olga Morozova
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data

2011

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Comparison of the Baseline Knowledge-, Corpus-, and Web-based Similarity Measures for Semantic Relations Extraction
Alexander Panchenko
Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics