AmAMorph: Finite State Morphological Analyzer for Amazighe

This paper presents AmAMorph, a morphological analyzer for Amazighe language using a system based on the NooJ linguistic development environment. The paper begins with the development of Amazighe lexicons with large coverage formalization. The built electronic lexicons, named ‘NAmLex’, ‘VAmLex’ and ‘PAmLex’ which stand for ‘Noun Amazighe Lexicon’, ‘Verb Amazighe Lexicon’ and ‘Particles Amazighe Lexicon’, link inflectional, morphological, and syntacticsemantic information to the list of lemmas. Automated inflectional and derivational routines are applied to each lemma producing over inflected forms. To our knowledge,AmAMorph is the first morphological analyzer for Amazighe. It identifies the component morphemes of the forms using large coverage morphological grammars. Along with the description of how the analyzer is implemented, this paper gives an evaluation of the analyzer

POS tagging in Amazigh using support vector machines and conditional random fields

The aim of this paper is to present the first Amazighe POS tagger. Very few linguistic resources have been developed so far for Amazighe and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. The used data have been manually collected and annotated. We have used state-of-art supervised machine learning approaches to build our POS-tagging models. The obtained accuracy achieved 92.58% and we have used the 10-fold technique to further validate our results. © Springer-Verlag Berlin Heidelberg 2011We would like to thank all IRCAM researchers for their valuable assistance. The work of the third author was funded by the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i).Outahajala, M.; Benajiba, Y.; Rosso, P.; Zenkouar, L. (2011). POS tagging in Amazigh using support vector machines and conditional random fields. En Natural Language Processing and Information Systems. Springer Verlag (Germany). 6716:238-241. https://doi.org/10.1007/978-3-642-22327-3_28S238241671

A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Creating language resources for under-resourced languages: methodologies, and experiments with Arabic

Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented

Abordagem modular baseada em dicionário para reconhecimento de entidades nomeadas através de associação aproximada

Orientador : Marcos Didonet Del FabroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 29/08/2016Inclui referências : f. 47-50Área de concentração: Ciência da computaçãoResumo: As técnicas de extração de informações estão sempre evoluindo para serem capazes de trabalhar com a quantidade crescente de dados disponíveis através de textos em linguagem natural e não estruturados. Destacamos a subtarefa da extração de informação conhecida como reconhecimento de entidades nomeadas baseado em dicionário, que realiza a identificação de sequências de caracteres que representam entidades de um determinado grupo, e o bom desempenho dessa subtarefa é fundamental para um bom processo de extração de informação. O reconhecimento de entidades nomeadas (NER) permite definir os sujeitos que são abordados pelo texto como organizações, pessoas, locais, etc. Pontos que ainda são desafios dentro da subtarefa de NER para sistemas baseados em dicionário são a presença de erros ortográficos nos textos e a existência de poucos sistemas de NER capazes de trabalhar em diferentes contextos. Esse trabalho apresenta uma abordagem para o reconhecimento de entidades nomeadas baseado em dicionário. Para trabalhar com textos que podem apresentar erros ortográficos, é utilizada uma busca por associação aproximada baseada na distância de edição entre as sequências de caracteres que representam a entrada do dicionário e as sub-partes do texto. Para promover a redução do erro entre as sequências de caracteres (SC) e facilitar a busca por associação aproximada são utilizados algoritmos de transformação. Esses algoritmos permitem a busca sobre o dicionário encontrar uma quantidade maior de entidades se comparada com as buscas utilizando as SCs originais para um mesmo valor da distância de edição aceita. As transformações também colaboram com a redução do tamanho das SCs e com a criação de mais prefixos similares, promovendo uma redução no tamanho da árvore de prefixo que indexa o dicionário. Para melhorar a precisão da nossa abordagem, disponibilizamos recursos de filtragem que fazem uso de métricas de similaridade para eliminar entidades falsas que foram retornadas da busca sobre o dicionário. Nossa abordagem também foi projetada para permitir a configuração de alguns componentes de forma a ser adaptada para diferentes casos de estudo. Palavras-chave: Reconhecimento de entidades nomeadas, Associação Aproximada de Sequências de Caracteres, Conversão fonética.Abstract: The information extraction techniques are always evolving to be able to work with the increasing amount of unstructured data available through texts in natural language. We highlight the information extraction subtask known as dictionary-based named entity recognition, which performs the identification of strings that represent entities of a particular group, and the good performance of this sub-task is critical for a good extracting information process. The named entity recognition (NER) defines the nouns that are covered by the text as organizations, people, places, etc. Some subjects that still represent chalenges in the sub-task of NER for currently systems that are dictionary-based are the presence of spelling errors in the text and the existence of few NER systems that are able to work in different contexts. This work presents an approach of a dictionary-based named entity recognition. Looking to work with texts that may have spelling errors, we use an approximate string matching search based on edit distance between the strings that represent the entries of the dictionary and the substrings of the text. To further the reduction of the error between the strings and facilitate the search using approximate matching we used transformation algorithms. These algorithms allow the search on the dictionary find a greater amount of entities if compared with the search using the original strings, for the same value of Edit Distance. Transformations also promote the strings size reduction and create more similar prefixes, promoting a reduction in the size of the prefix tree (trie) that indexes the dictionary. To improve the precision of our approach, we provide filtering capabilities that make use of similarity metrics to eliminate false entities that have been returned from the search on the dictionary trie. Our approach is also designed to enable the configuration of some components to be adapted to different study cases. Keywords: Named entity recognition, Approximate string matching, Phonetic conversion

Entanglements of the Maghreb

The impulse for the recent transformations in the Arab world came from the Maghreb. Research on the region has been on the rise since, yet much remains to be done when it comes to interdisciplinary comparative research. The Maghreb is a heterogeneous region that deserves thorough investigation. This volume focuses on Entanglements as a cross-field and cross-lingual concept to generate a new approach to the region and its inner interdependencies as well as exchanges with other regions. Eminent researchers conceptualize Entanglements through the description of various thematic fields and actors in motion, addressing culture, politics, social affairs, and economics

Entanglements of the Maghreb: Cultural and Political Aspects of a Region in Motion

The impulse for the recent transformations in the Arab world came from the Maghreb. Research on the region has been on the rise since, yet much remains to be done when it comes to interdisciplinary comparative research. The Maghreb is a heterogeneous region that deserves thorough investigation. This volume focuses on Entanglements as a cross-field and cross-lingual concept to generate a new approach to the region and its inner interdependencies as well as exchanges with other regions. Eminent researchers conceptualize Entanglements through the description of various thematic fields and actors in motion, addressing culture, politics, social affairs, and economics