Automatic Idiom Identification in Wiktionary

Online resources, such as Wiktionary, provide an accurate but incomplete source of idiomatic phrases. In this paper, we study the problem of automatically identifying idiomatic dictionary entries with such resources. We train an idiom classifier on a newly gathered corpus of over 60,000 Wiktionary multi-word definitions, incorporating features that model whether phrase meanings are constructed compositionally. Experiments demonstrate that the learned classifier can provide high quality idiom labels, more than doubling the number of idiomatic entries from 7,764 to 18,155 at precision levels of over 65%. These gains also translate to idiom detection in sentences, by simply using known word sense disambiguation algorithms to match phrases to their definitions. In a set of Wiktionary definition example sentences, the more complete set of idioms boosts detection recall by over 28 percentage points.

Examining the Tip of the Iceberg: A Data Set for Idiom Translation

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains one of the open challenges in this field. Idioms, a category of multiword expressions, are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts. A first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation. In this paper we address this problem by creating the first large-scale data set for idiom translation. Our data set is automatically extracted from a widely used German-English translation corpus and includes, for each language direction, a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked. We release this data set and use it to perform preliminary NMT experiments as the first step towards better idiom translation.Comment: Accepted at LREC 201

A Bigger Fish to Fry:Scaling up the Automatic Understanding of Idiomatic Expressions

In this thesis, we are concerned with idiomatic expressions and how to handle them within NLP. Idiomatic expressions are a type of multiword phrase which have a meaning that is not a direct combination of the meaning of its parts, e.g. 'at a crossroads' and 'move the goalposts'.In Part I, we provide a general introduction to idiomatic expressions and an overview of observations regarding idioms based on corpus data. In addition, we discuss existing research on idioms from an NLP perspective, providing an overview of existing tasks, approaches, and datasets. In Part II, we focus on the building of a large idiom corpus, consisting of developing a system for the automatic extraction of potentially idiom expressions and building a large corpus of idiom using crowdsourced annotation. Finally, in Part III, we improve an existing unsupervised classifier and compare it to other existing classifiers. Given the relatively poor performance of this unsupervised classifier, we also develop a supervised deep neural network-based system and find that a model involving two separate modules looking at different information sources yields the best performance, surpassing previous state-of-the-art approaches.In conclusion, this work shows the feasibility of building a large corpus of sense-annotated potentially idiomatic expressions, and the benefits such a corpus provides for further research. It provides the possibility for quick testing of hypotheses about the distribution and usage of idioms, it enables the training of data-hungry machine learning methods for PIE disambiguation systems, and it permits fine-grained, reliable evaluation of such systems

How many people constitute a crowd and what do they do? Quantitative analyses of revisions in the English and German wiktionary editions

Wiktionary is increasingly gaining influence in a wide variety of linguistic fields such as NLP and lexicography, and has great potential to become a serious competitor for publisher-based and academic dictionaries. However, little is known about the "crowd" that is responsible for the content of Wiktionary. In this article, we want to shed some light on selected questions con-cerning large-scale cooperative work in online dictionaries. To this end, we use quantitative analy-ses of the complete edit history files of the English and German Wiktionary language editions. Concerning the distribution of revisions over users, we show that — compared to the overall user base — only very few authors are responsible for the vast majority of revisions in the two Wiktion-ary editions. In the next step, we compare this distribution to the distribution of revisions over all the articles. The articles are subsequently analysed in terms of rigour and diversity, typical revision patterns through time, and novelty (the time since the last revision). We close with an examination of the relationship between corpus frequencies of headwords in articles, the number of article vis-its, and the number of revisions made to articles.Keywords: User-Generated Content, Online Dictionary, Wiktionary, Revision, Edit, Frequency, Collaboration, Wisdom of The Crow

ID10M: Idiom Identification in 10 Languages

Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks. Although efforts have been undertaken in this direction, the automatic identification and understanding of idioms is still a largely under-investigated area, especially when operating in a multilingual scenario. In this paper, we address such limitations and put forward several new contributions: we propose a novel multilingual Transformer-based system for the identification of idioms; we produce a high-quality automatically-created training dataset in 10 languages, along with a novel manually-curated evaluation benchmark; finally, we carry out a thorough performance analysis and release our evaluation suite at https://github.com/Babelscape/ID10M