Morphological, syntactic and diacritics rules for automatic diacritization of Arabic sentences

AbstractThe diacritical marks of Arabic language are characters other than letters and are in the majority of cases absent from Arab writings. This paper presents a hybrid system for automatic diacritization of Arabic sentences combining linguistic rules and statistical treatments. The used approach is based on four stages. The first phase consists of a morphological analysis using the second version of the morphological analyzer Alkhalil Morpho Sys. Morphosyntactic outputs from this step are used in the second phase to eliminate invalid word transitions according to the syntactic rules. Then, the system used in the third stage is a discrete hidden Markov model and Viterbi algorithm to determine the most probable diacritized sentence. The unseen transitions in the training corpus are processed using smoothing techniques. Finally, the last step deals with words not analyzed by Alkhalil analyzer, for which we use statistical treatments based on the letters. The word error rate of our system is around 2.58% if we ignore the diacritic of the last letter of the word and around 6.28% when this diacritic is taken into account

Dynamic language modeling for European Portuguese

Doutoramento em Engenharia InformáticaActualmente muitas das metodologias utilizadas para transcrição e indexação de transmissões noticiosas são baseadas em processos manuais. Com o processamento e transcrição deste tipo de dados os prestadores de serviços noticiosos procuram extrair informação semântica que permita a sua interpretação, sumarização, indexação e posterior disseminação selectiva. Pelo que, o desenvolvimento e implementação de técnicas automáticas para suporte deste tipo de tarefas têm suscitado ao longo dos últimos anos o interesse pela utilização de sistemas de reconhecimento automático de fala. Contudo, as especificidades que caracterizam este tipo de tarefas, nomeadamente a diversidade de tópicos presentes nos blocos de notícias, originam um elevado número de ocorrência de novas palavras não incluídas no vocabulário finito do sistema de reconhecimento, o que se traduz negativamente na qualidade das transcrições automáticas produzidas pelo mesmo. Para línguas altamente flexivas, como é o caso do Português Europeu, este problema torna-se ainda mais relevante. Para colmatar este tipo de problemas no sistema de reconhecimento, várias abordagens podem ser exploradas: a utilização de informações específicas de cada um dos blocos noticiosos a ser transcrito, como por exemplo os scripts previamente produzidos pelo pivot e restantes jornalistas, e outro tipo de fontes como notícias escritas diariamente disponibilizadas na Internet. Este trabalho engloba essencialmente três contribuições: um novo algoritmo para selecção e optimização do vocabulário, utilizando informação morfosintáctica de forma a compensar as diferenças linguísticas existentes entre os diferentes conjuntos de dados; uma metodologia diária para adaptação dinâmica e não supervisionada do modelo de linguagem, utilizando múltiplos passos de reconhecimento; metodologia para inclusão de novas palavras no vocabulário do sistema, mesmo em situações de não existência de dados de adaptação e sem necessidade re-estimação global do modelo de linguagem.Most of today methods for transcription and indexation of broadcast audio data are manual. Broadcasters process thousands hours of audio and video data on a daily basis, in order to transcribe that data, to extract semantic information, and to interpret and summarize the content of those documents. The development of automatic and efficient support for these manual tasks has been a great challenge and over the last decade there has been a growing interest in the usage of automatic speech recognition as a tool to provide automatic transcription and indexation of broadcast news and random and relevant access to large broadcast news databases. However, due to the common topic changing over time which characterizes this kind of tasks, the appearance of new events leads to high out-of-vocabulary (OOV) word rates and consequently to degradation of recognition performance. This is especially true for highly inflected languages like the European Portuguese language. Several innovative techniques can be exploited to reduce those errors. The use of news shows specific information, such as topic-based lexicons, pivot working script, and other sources such as the online written news daily available in the Internet can be added to the information sources employed by the automatic speech recognizer. In this thesis we are exploring the use of additional sources of information for vocabulary optimization and language model adaptation of a European Portuguese broadcast news transcription system. Hence, this thesis has 3 different main contributions: a novel approach for vocabulary selection using Part-Of-Speech (POS) tags to compensate for word usage differences across the various training corpora; language model adaptation frameworks performed on a daily basis for single-stage and multistage recognition approaches; a new method for inclusion of new words in the system vocabulary without the need of additional data or language model retraining

Context-Aware Prediction of Derivational Word-forms

Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose the new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder--decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under a lexicon agnostic setting

Methods and algorithms for unsupervised learning of morphology

This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15 The accepted version of the publication may differ from the final published version.This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Published versio

Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords

Word vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities. In this paper, we propose a novel word vector space estimation by unsupervised learning on lattices output by an automatic speech recognition (ASR) system. We encode each word in confusion2vec vector space by its constituent subword character n-grams. We show the subword encoding helps better represent the acoustic perceptual ambiguities in human spoken language via information modeled on lattice structured ASR output. The usefulness of the proposed Confusion2vec representation is evaluated using semantic, syntactic and acoustic analogy and word similarity tasks. We also show the benefits of subword modeling for acoustic ambiguity representation on the task of spoken language intent detection. The results significantly outperform existing word vector representations when evaluated on erroneous ASR outputs. We demonstrate that Confusion2vec subword modeling eliminates the need for retraining/adapting the natural language understanding models on ASR transcripts

Coupling an annotated corpus and a lexicon for state-of-the-art POS tagging

International audienceThis paper investigates how to best couple hand-annotated data with information extracted from an external lexical resource to improve POS tagging performance. Focusing on French tagging, we introduce a maximum entropy conditional sequence tagging system that is enriched with information extracted from a morphological resource. This system gives a 97.7% accuracy on the French Treebank, an error reduction of 23% (28% on unknown words) over the same tagger without lexical information. We also conduct experiments on datasets and lexicons of varying sizes in order to assess the best trade-off between annotating data vs. developing a lexicon. We find that the use of a lexicon improves the quality of the tagger at any stage of development of either resource, and that for fixed performance levels the availability of the full lexicon consistently reduces the need for supervised data by at least one half

Adding new words into a language model using parameters of known words with similar behavior

International audienceThis article presents a study on how to automatically add new words into a language model without retraining it or adapting it (which requires a lot of new data). The proposed approach consists in finding a list of similar words for each new word to be added in the language model. Based on a small set of sentences containing the new words and on a set of n-gram counts containing the known words, we search for known words which have the most similar neighbor distribution (of the few preceding and few following neighbor words) to the new words. The similar words are determined through the computation of KL divergences on the distribution of neighbor words. The n-gram parameter values associated to the similar words are then used to define the n-gram parameter values of the new words. In the context of speech recognition, the performance assessment on a LVCSR task shows the benefit of the proposed approach

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge