Automatic extraction of Arabic multiword expressions

In this paper we investigate the automatic acquisition of Arabic Multiword Expressions (MWE). We propose three complementary approaches to extract MWEs from available data resources. The first approach relies on the correspondence asymmetries between Arabic Wikipedia titles and titles in 21 different languages. The second approach collects English MWEs from Princeton WordNet 3.0, translates the collection into Arabic using Google Translate, and utilizes different search engines to validate the output. The third uses lexical association measures to extract MWEs from a large unannotated corpus. We experimentally explore the feasibility of each approach and measure the quality and coverage of the output against gold standards

MultiMWE: building a multi-lingual multi-word expression (MWE) parallel corpora

Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features

Towards Comprehensive Computational Representations of Arabic Multiword Expressions

A successful computational treatment of multiword expressions (MWEs) in natural languages leads to a robust NLP system which considers the long-standing problem of language ambiguity caused primarily by this complex linguistic phenomenon. The first step in addressing this challenge is building an extensive reliable MWEs language resource LR with comprehensive computational representations across all linguistic levels. This forms the cornerstone in understanding the heterogeneous linguistic behaviour of MWEs in their various manifestations. This paper presents a detailed framework for computational representations of Arabic MWEs (ArMWEs) across all linguistic levels based on the state-of-the-art lexical mark-up framework (LMF) with the necessary modifications to suit the distinctive properties of Modern Standard Arabic (MSA). This work forms part of a larger project that aims to develop a comprehensive computational lexicon of ArMWEs for NLP and language pedagogy LP (JOMAL project)

Towards Comprehensive Computational Representations of Arabic Multiword Expressions

A successful computational treatment of multiword expressions (MWEs) in natural languages leads to a robust NLP system which considers the long-standing problem of language ambiguity caused primarily by this complex linguistic phenomenon. The first step in addressing this challenge is building an extensive reliable MWEs language resource LR with comprehensive computational representations across all linguistic levels. This forms the cornerstone in understanding the heterogeneous linguistic behaviour of MWEs in their various manifestations. This paper presents a detailed framework for computational representations of Arabic MWEs (ArMWEs) across all linguistic levels based on the state-of-the-art lexical mark-up framework (LMF) with the necessary modifications to suit the distinctive properties of Modern Standard Arabic (MSA). This work forms part of a larger project that aims to develop a comprehensive computational lexicon of ArMWEs for NLP and language pedagogy LP (JOMAL project)

Normalized Google Distance for Collocation Extraction from Islamic Domain

This study investigates the properties of Arabic collocations, and classifies them according to their structural patterns on Islamic domain. Based on linguistic information, the patterns and the variation of the collocations have been identified.  Then, a system that extracts the collocations from Islamic domain based on statistical measures has been described. In candidate ranking, the normalized Google distance has been adapted to measure the associations between the words in the candidates set. Finally, the n-best evaluation that selects n-best lists for each association measure has been used to annotate all candidates in these lists manually. The following association measures (log-likelihood ratio, t-score, mutual information, and enhanced mutual information) have been utilized in the candidate ranking step to compare these measures with the normalized Google distance in Arabic collocation extraction. In the experiment of this work, the normalized Google distance achieved the highest precision value 93% compared with other association measures. In fact, this strengthens our motivation to utilize the normalized Google distance to measure the relatedness between the constituent words of the collocations instead of using the frequency-based association measures as in the state-of-the-art methods. Keywords: normalized Google distance, collocation extraction, Islamic domai

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