Els Lefever


2021

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A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek
Pranaydeep Singh | Gorik Rutten | Els Lefever
Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT

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LT3 at SemEval-2021 Task 6: Using Multi-Modal Compact Bilinear Pooling to Combine Visual and Textual Understanding in Memes
Pranaydeep Singh | Els Lefever
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Internet memes have become ubiquitous in social media networks today. Due to their popularity, they are also a widely used mode of expression to spread disinformation online. As memes consist of a mixture of text and image, they require a multi-modal approach for automatic analysis. In this paper, we describe our contribution to the SemEval-2021 Detection of Persuasian Techniques in Texts and Images Task. We propose a Multi-Modal learning system, which incorporates “memebeddings”, viz. joint text and vision features by combining them with compact bilinear pooling, to automatically identify rhetorical and psychological disinformation techniques. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and achieves the 2nd best Macro F1-score and 14th best Micro F1-score out of all participants.

2020

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LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines
Bram Vanroy | Sofie Labat | Olha Kaminska | Els Lefever | Veronique Hoste
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines. Our first system is a feature-based machine learning system that combines different types of information (e.g. word embeddings, string similarity, part-of-speech tags, perplexity scores, named entity recognition) in a Nu Support Vector Regressor (NuSVR). The second system is a deep learning-based approach that uses the pre-trained language model RoBERTa to learn latent features in the news headlines that are useful to predict the funniness of each headline. The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253.

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LT3 at SemEval-2020 Task 8: Multi-Modal Multi-Task Learning for Memotion Analysis
Pranaydeep Singh | Nina Bauwelinck | Els Lefever
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval-2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.

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LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text
Pranaydeep Singh | Els Lefever
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52% on the held-out test data.

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Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings
Pranaydeep Singh | Els Lefever
Proceedings of the The 4th Workshop on Computational Approaches to Code Switching

This paper investigates the use of unsupervised cross-lingual embeddings for solving the problem of code-mixed social media text understanding. We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz. English combined with (transliterated) Hindi. In a first step, baseline models, initialized with monolingual embeddings obtained from large collections of tweets in English and code-mixed Hinglish, were trained. In a second step, two systems using cross-lingual embeddings were researched, being (1) a supervised classifier and (2) a transfer learning approach trained on English sentiment data and evaluated on code-mixed data. We demonstrate that incorporating cross-lingual embeddings improves the results (F1-score of 0.635 versus a monolingual baseline of 0.616), without any parallel data required to train the cross-lingual embeddings. In addition, the results show that the cross-lingual embeddings not only improve the results in a fully supervised setting, but they can also be used as a base for distant supervision, by training a sentiment model in one of the source languages and evaluating on the other language projected in the same space. The transfer learning experiments result in an F1-score of 0.556, which is almost on par with the supervised settings and speak to the robustness of the cross-lingual embeddings approach.

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Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study
Nina Bauwelinck | Els Lefever
Proceedings of the 7th Workshop on Argument Mining

One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.

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TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset
Ayla Rigouts Terryn | Veronique Hoste | Patrick Drouin | Els Lefever
Proceedings of the 6th International Workshop on Computational Terminology

The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.

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Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings
Els Lefever | Sofie Labat | Pranaydeep Singh
Proceedings of the 12th Language Resources and Evaluation Conference

This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch. In a first step, lists of potential cognate pairs in English-Dutch and French-Dutch are manually labelled. The resulting gold standard is used to train and evaluate a multi-layer perceptron that can distinguish cognates from non-cognates. Fifteen orthographic features capture string similarities between source and target words, while the cosine similarity between their word embeddings represents the semantic relation between these words. By adding domain-specific information to pretrained fastText embeddings, we are able to obtain good embeddings for words that did not yet have a pretrained embedding (e.g. Dutch compound nouns). These embeddings are then aligned in a cross-lingual vector space by exploiting their structural similarity (cf. adversarial learning). Our results indicate that although the classifier already achieves good results on the basis of orthographic information, the performance further improves by including semantic information in the form of cross-lingual word embeddings.

2019

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LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)
Nina Bauwelinck | Gilles Jacobs | Véronique Hoste | Els Lefever
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval). We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language. Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.

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A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information
Sofie Labat | Els Lefever
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

This paper presents proof-of-concept experiments for combining orthographic and semantic information to distinguish cognates from non-cognates. To this end, a context-independent gold standard is developed by manually labelling English-Dutch pairs of cognates and false friends in bilingual term lists. These annotated cognate pairs are then used to train and evaluate a supervised binary classification system for the automatic detection of cognates. Two types of information sources are incorporated in the classifier: fifteen string similarity metrics capture form similarity between source and target words, while word embeddings model semantic similarity between the words. The experimental results show that even though the system already achieves good results by only incorporating orthographic information, the performance further improves by including semantic information in the form of embeddings.

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Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat
Ayla Rigouts Terryn | Patrick Drouin | Veronique Hoste | Els Lefever
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.

2018

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We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter
Cynthia Van Hee | Els Lefever | Véronique Hoste
Computational Linguistics, Volume 44, Issue 4 - December 2018

Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.

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A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents
Ayla Rigouts Terryn | Véronique Hoste | Els Lefever
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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MIsA: Multilingual “IsA” Extraction from Corpora
Stefano Faralli | Els Lefever | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Discovering the Language of Wine Reviews: A Text Mining Account
Els Lefever | Iris Hendrickx | Ilja Croijmans | Antal van den Bosch | Asifa Majid
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Economic Event Detection in Company-Specific News Text
Gilles Jacobs | Els Lefever | Véronique Hoste
Proceedings of the First Workshop on Economics and Natural Language Processing

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups

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SemEval-2018 Task 3: Irony Detection in English Tweets
Cynthia Van Hee | Els Lefever | Véronique Hoste
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. #irony, #sarcasm, #not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.

2017

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Towards an integrated pipeline for aspect-based sentiment analysis in various domains
Orphée De Clercq | Els Lefever | Gilles Jacobs | Tijl Carpels | Véronique Hoste
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, banking and human resources. The two latter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA subtasks, aspect term extraction, aspect category classification and aspect polarity classification.

2016

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SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Georgeta Bordea | Els Lefever | Paul Buitelaar
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets
Cynthia Van Hee | Els Lefever | Véronique Hoste
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).

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A Classification-based Approach to Economic Event Detection in Dutch News Text
Els Lefever | Véronique Hoste
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.

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Exploring the Realization of Irony in Twitter Data
Cynthia Van Hee | Els Lefever | Véronique Hoste
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.

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Very quaffable and great fun: Applying NLP to wine reviews
Iris Hendrickx | Els Lefever | Ilja Croijmans | Asifa Majid | Antal van den Bosch
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally
Cynthia Van Hee | Els Lefever | Véronique Hoste
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis
Orphée De Clercq | Marjan Van de Kauter | Els Lefever | Véronique Hoste
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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LT3: A Multi-modular Approach to Automatic Taxonomy Construction
Els Lefever
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Detection and Fine-Grained Classification of Cyberbullying Events
Cynthia Van Hee | Els Lefever | Ben Verhoeven | Julie Mennes | Bart Desmet | Guy De Pauw | Walter Daelemans | Veronique Hoste
Proceedings of the International Conference Recent Advances in Natural Language Processing

2014

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SemEval 2014 Task 5 - L2 Writing Assistant
Maarten van Gompel | Iris Hendrickx | Antal van den Bosch | Els Lefever | Véronique Hoste
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set
Cynthia Van Hee | Marjan Van de Kauter | Orphée De Clercq | Els Lefever | Véronique Hoste
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Evaluation of Automatic Hypernym Extraction from Technical Corpora in English and Dutch
Els Lefever | Marjan Van de Kauter | Véronique Hoste
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this research, we evaluate different approaches for the automatic extraction of hypernym relations from English and Dutch technical text. The detected hypernym relations should enable us to semantically structure automatically obtained term lists from domain- and user-specific data. We investigated three different hypernymy extraction approaches for Dutch and English: a lexico-syntactic pattern-based approach, a distributional model and a morpho-syntactic method. To test the performance of the different approaches on domain-specific data, we collected and manually annotated English and Dutch data from two technical domains, viz. the dredging and financial domain. The experimental results show that especially the morpho-syntactic approach obtains good results for automatic hypernym extraction from technical and domain-specific texts.

2013

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Normalization of Dutch User-Generated Content
Orphée De Clercq | Sarah Schulz | Bart Desmet | Els Lefever | Véronique Hoste
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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A Combined Pattern-based and Distributional Approach for Automatic Hypernym Detection in Dutch.
Gwendolijn Schropp | Els Lefever | Véronique Hoste
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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SemEval-2013 Task 10: Cross-lingual Word Sense Disambiguation
Els Lefever | Véronique Hoste
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Discovering Missing Wikipedia Inter-language Links by means of Cross-lingual Word Sense Disambiguation
Els Lefever | Véronique Hoste | Martine De Cock
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Wikipedia pages typically contain inter-language links to the corresponding pages in other languages. These links, however, are often incomplete. This paper describes a set of experiments in which the viability of discovering such missing inter-language links for ambiguous nouns by means of a cross-lingual Word Sense Disambiguation approach is investigated. The input for the inter-language link detection system is a set of Dutch pages for a given ambiguous noun and the output of the system is a set of links to the corresponding pages in three target languages (viz. French, Spanish and Italian). The experimental results show that although it is a very challenging task, the system succeeds to detect missing inter-language links between Wikipedia documents for a manually labeled test set. The final goal of the system is to provide a human editor with a list of possible missing links that should be manually verified.

2011

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ParaSense or How to Use Parallel Corpora for Word Sense Disambiguation
Els Lefever | Véronique Hoste | Martine De Cock
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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An Evaluation and Possible Improvement Path for Current SMT Behavior on Ambiguous Nouns
Els Lefever | Véronique Hoste
Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation

2010

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SemEval-2010 Task 3: Cross-Lingual Word Sense Disambiguation
Els Lefever | Veronique Hoste
Proceedings of the 5th International Workshop on Semantic Evaluation

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Construction of a Benchmark Data Set for Cross-lingual Word Sense Disambiguation
Els Lefever | Véronique Hoste
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Given the recent trend to evaluate the performance of word sense disambiguation systems in a more application-oriented set-up, we report on the construction of a multilingual benchmark data set for cross-lingual word sense disambiguation. The data set was created for a lexical sample of 25 English nouns, for which translations were retrieved in 5 languages, namely Dutch, German, French, Italian and Spanish. The corpus underlying the sense inventory was the parallel data set Europarl. The gold standard sense inventory was based on the automatic word alignments of the parallel corpus, which were manually verified. The resulting word alignments were used to perform a manual clustering of the translations over all languages in the parallel corpus. The inventory then served as input for the annotators of the sentences, who were asked to provide a maximum of three contextually relevant translations per language for a given focus word. The data set was released in the framework of the SemEval-2010 competition.

2009

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SemEval-2010 Task 3: Cross-lingual Word Sense Disambiguation
Els Lefever | Veronique Hoste
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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Language-Independent Bilingual Terminology Extraction from a Multilingual Parallel Corpus
Els Lefever | Lieve Macken | Veronique Hoste
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

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Learning-based Detection of Scientific Terms in Patient Information
Veronique Hoste | Els Lefever | Klaar Vanopstal | Isabelle Delaere
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we investigate the use of a machine-learning based approach to the specific problem of scientific term detection in patient information. Lacking lexical databases which differentiate between the scientific and popular nature of medical terms, we used local context, morphosyntactic, morphological and statistical information to design a learner which accurately detects scientific medical terms. This study is the first step towards the automatic replacement of a scientific term by its popular counterpart, which should have a beneficial effect on readability. We show a F-score of 84% for the prediction of scientific terms in an English and Dutch EPAR corpus. Since recasting the term extraction problem as a classification problem leads to a large skewedness of the resulting data set, we rebalanced the data set through the application of some simple TF-IDF-based and Log-likelihood-based filters. We show that filtering indeed has a beneficial effect on the learner’s performance. However, the results of the filtering approach combined with the learning-based approach remain below those of the learning-based approach.

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Linguistically-Based Sub-Sentential Alignment for Terminology Extraction from a Bilingual Automotive Corpus
Lieve Macken | Els Lefever | Veronique Hoste
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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AUG: A combined classification and clustering approach for web people disambiguation
Els Lefever | Véronique Hoste | Timur Fayruzov
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)