Russian Lexicographic Landscape: a Tale of 12 Dictionaries

The paper reports on quantitative analysis of 12 Russian dictionaries at three levels: 1) headwords: The size and overlap of word lists, coverage of large corpora, and presence of neologisms; 2) synonyms: Overlap of synsets in different dictionaries; 3) definitions: Distribution of definition lengths and numbers of senses, as well as textual similarity of same-headword definitions in different dictionaries. The total amount of data in the study is 805,900 dictionary entries, 892,900 definitions, and 84,500 synsets. The study reveals multiple connections and mutual influences between dictionaries, uncovers differences in modern electronic vs. traditional printed resources, as well as suggests directions for development of new and improvement of existing lexical semantic resources

The Other Side of the Coin:Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses

Disambiguation of potentially idiomatic expressions involves determining the sense of a potentially idiomatic expression in a given context, e.g. determining that make hay in ‘Investment banks made hay while takeovers shone.’ is used in a figurative sense. This enables automatic interpretation of idiomatic expressions, which is important for applications like machine translation and sentiment analysis. In this work, we present an unsupervised approach for English that makes use of literalisations of idiom senses to improve disambiguation, which is based on the lexical cohesion graph-based method by Sporleder and Li (2009). Experimental results show that, while literalisation carries novel information, its performance falls short of that of state-of-the-artunsupervised methods

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

Annotation of multiword expressions in French

International audienceThis paper presents an experiment of annotation of MWEs in French. The corpus used is made of several genres (news, novel, scientific report, film subtitles) and includes a rich annotation scheme including several kinds of MWEs from collocations to routines and full phrasemes. The annotation is performed semi-automatically with finite-state transducers. The inter-annotator agreement score shows that the annotation is quite consistent but the difficulty of the task relies heavily on the textual genre: literary texts are harder to annotate than scientific reports. Besides, two types of categories are difficult to differentiate, collocations and full phrasemes