An Urdu semantic tagger - lexicons, corpora, methods and tools

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports $F_1$ of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F$_1$ of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%

Discourse analysis of arabic documents and application to automatic summarization

Dans un discours, les textes et les conversations ne sont pas seulement une juxtaposition de mots et de phrases. Ils sont plutôt organisés en une structure dans laquelle des unités de discours sont liées les unes aux autres de manière à assurer à la fois la cohérence et la cohésion du discours. La structure du discours a montré son utilité dans de nombreuses applications TALN, y compris la traduction automatique, la génération de texte et le résumé automatique. L'utilité du discours dans les applications TALN dépend principalement de la disponibilité d'un analyseur de discours performant. Pour aider à construire ces analyseurs et à améliorer leurs performances, plusieurs ressources ont été annotées manuellement par des informations de discours dans des différents cadres théoriques. La plupart des ressources disponibles sont en anglais. Récemment, plusieurs efforts ont été entrepris pour développer des ressources discursives pour d'autres langues telles que le chinois, l'allemand, le turc, l'espagnol et le hindi. Néanmoins, l'analyse de discours en arabe standard moderne (MSA) a reçu moins d'attention malgré le fait que MSA est une langue de plus de 422 millions de locuteurs dans 22 pays. Le sujet de thèse s'intègre dans le cadre du traitement automatique de la langue arabe, plus particulièrement, l'analyse de discours de textes arabes. Cette thèse a pour but d'étudier l'apport de l'analyse sémantique et discursive pour la génération de résumé automatique de documents en langue arabe. Pour atteindre cet objectif, nous proposons d'étudier la théorie de la représentation discursive segmentée (SDRT) qui propose un cadre logique pour la représentation sémantique de phrases ainsi qu'une représentation graphique de la structure du texte où les relations de discours sont de nature sémantique plutôt qu'intentionnelle. Cette théorie a été étudiée pour l'anglais, le français et l'allemand mais jamais pour la langue arabe. Notre objectif est alors d'adapter la SDRT à la spécificité de la langue arabe afin d'analyser sémantiquement un texte pour générer un résumé automatique. Nos principales contributions sont les suivantes : Une étude de la faisabilité de la construction d'une structure de discours récursive et complète de textes arabes. En particulier, nous proposons : Un schéma d'annotation qui couvre la totalité d'un texte arabe, dans lequel chaque constituant est lié à d'autres constituants. Un document est alors représenté par un graphe acyclique orienté qui capture les relations explicites et les relations implicites ainsi que des phénomènes de discours complexes, tels que l'attachement, la longue distance du discours pop-ups et les dépendances croisées. Une nouvelle hiérarchie des relations de discours. Nous étudions les relations rhétoriques d'un point de vue sémantique en se concentrant sur leurs effets sémantiques et non pas sur la façon dont elles sont déclenchées par des connecteurs de discours, qui sont souvent ambigües en arabe. o une analyse quantitative (en termes de connecteurs de discours, de fréquences de relations, de proportion de relations implicites, etc.) et une analyse qualitative (accord inter-annotateurs et analyse des erreurs) de la campagne d'annotation. Un outil d'analyse de discours où nous étudions à la fois la segmentation automatique de textes arabes en unités de discours minimales et l'identification automatique des relations explicites et implicites du discours. L'utilisation de notre outil pour résumer des textes arabes. Nous comparons la représentation de discours en graphes et en arbres pour la production de résumés.Within a discourse, texts and conversations are not just a juxtaposition of words and sentences. They are rather organized in a structure in which discourse units are related to each other so as to ensure both discourse coherence and cohesion. Discourse structure has shown to be useful in many NLP applications including machine translation, natural language generation and language technology in general. The usefulness of discourse in NLP applications mainly depends on the availability of powerful discourse parsers. To build such parsers and improve their performances, several resources have been manually annotated with discourse information within different theoretical frameworks. Most available resources are in English. Recently, several efforts have been undertaken to develop manually annotated discourse information for other languages such as Chinese, German, Turkish, Spanish and Hindi. Surprisingly, discourse processing in Modern Standard Arabic (MSA) has received less attention despite the fact that MSA is a language with more than 422 million speakers in 22 countries. Computational processing of Arabic language has received a great attention in the literature for over twenty years. Several resources and tools have been built to deal with Arabic non concatenative morphology and Arabic syntax going from shallow to deep parsing. However, the field is still very vacant at the layer of discourse. As far as we know, the sole effort towards Arabic discourse processing was done in the Leeds Arabic Discourse Treebank that extends the Penn Discourse TreeBank model to MSA. In this thesis, we propose to go beyond the annotation of explicit relations that link adjacent units, by completely specifying the semantic scope of each discourse relation, making transparent an interpretation of the text that takes into account the semantic effects of discourse relations. In particular, we propose the first effort towards a semantically driven approach of Arabic texts following the Segmented Discourse Representation Theory (SDRT). Our main contributions are: A study of the feasibility of building a recursive and complete discourse structures of Arabic texts. In particular, we propose: An annotation scheme for the full discourse coverage of Arabic texts, in which each constituent is linked to other constituents. A document is then represented by an oriented acyclic graph, which captures explicit and implicit relations as well as complex discourse phenomena, such as long-distance attachments, long-distance discourse pop-ups and crossed dependencies. A novel discourse relation hierarchy. We study the rhetorical relations from a semantic point of view by focusing on their effect on meaning and not on how they are lexically triggered by discourse connectives that are often ambiguous, especially in Arabic. A thorough quantitative analysis (in terms of discourse connectives, relation frequencies, proportion of implicit relations, etc.) and qualitative analysis (inter-annotator agreements and error analysis) of the annotation campaign. An automatic discourse parser where we investigate both automatic segmentation of Arabic texts into elementary discourse units and automatic identification of explicit and implicit Arabic discourse relations. An application of our discourse parser to Arabic text summarization. We compare tree-based vs. graph-based discourse representations for producing indicative summaries and show that the full discourse coverage of a document is definitively a plus

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal