The Summarization of Arabic News Texts Using Probabilistic Topic Modeling for L2 Micro Learning Tasks

Report submitted as a result, in part, of participation in the Language Flagship Technology Innovation Center's Summer internship program in Summer 2019.The field of Natural Language Processing (NLP) combines computer science, linguistic theory, and mathematics. Natural Language Processing applications aim at equipping computers with human linguistic knowledge. Applications such as Information Retrieval, Machine Translation, spelling checkers, as well as text sum- marization, are intriguing fields that exploit the techniques of NLP. Text summariza- tion represents an important NLP task that simplifies various reading tasks. These NLP-based text summarization tasks can be utilized for the benefits of language acquisition.Language Flagship Technology Innovation Cente

Mining Twitter for crisis management: realtime floods detection in the Arabian Peninsula

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.In recent years, large amounts of data have been made available on microblog platforms such as Twitter, however, it is difficult to filter and extract information and knowledge from such data because of the high volume, including noisy data. On Twitter, the general public are able to report real-world events such as floods in real time, and act as social sensors. Consequently, it is beneficial to have a method that can detect flood events automatically in real time to help governmental authorities, such as crisis management authorities, to detect the event and make decisions during the early stages of the event. This thesis proposes a real time flood detection system by mining Arabic Tweets using machine learning and data mining techniques. The proposed system comprises five main components: data collection, pre-processing, flooding event extract, location inferring, location named entity link, and flooding event visualisation. An effective method of flood detection from Arabic tweets is presented and evaluated by using supervised learning techniques. Furthermore, this work presents a location named entity inferring method based on the Learning to Search method, the results show that the proposed method outperformed the existing systems with significantly higher accuracy in tasks of inferring flood locations from tweets which are written in colloquial Arabic. For the location named entity link, a method has been designed by utilising Google API services as a knowledge base to extract accurate geocode coordinates that are associated with location named entities mentioned in tweets. The results show that the proposed location link method locate 56.8% of tweets with a distance range of 0 – 10 km from the actual location. Further analysis has shown that the accuracy in locating tweets in an actual city and region are 78.9% and 84.2% respectively

Indización automática de artículos científicos sobre Biblioteconomía y Documentación con SISA, KEA y MAUI

This article evaluates the SISA (Automatic Indexing System), KEA (Keyphrase Extraction Algorithm) and MAUI (Multi-Purpose Automatic Topic Indexing) automatic indexing systems to find out how they perform in relation to human indexing. SISA’s algorithm is based on rules about the position of terms in the different structural components of the document, while the algorithms for KEA and MAUI are based on machine learning and the statistical features of terms. For evaluation purposes, a document collection of 230 scientific articles from the Revista Española de Documentación Científica published by the Consejo Superior de Investigaciones Científicas (CSIC) was used, of which 30 were used for training tasks and were not part of the evaluation test set. The articles were written in Spanish and indexed by human indexers using a controlled vocabulary in the InDICES database, also belonging to the CSIC. The human indexing of these documents constitutes the baseline or golden indexing, against which to evaluate the output of the automatic indexing systems by comparing terms sets using the evaluation metrics of precision, recall, F-measure and consistency. The results show that the SISA system performs best, followed by KEA and MAUI.Este artículo evalúa los sistemas de indización automática SISA (Automatic Indexing System), KEA (Keyphrase Extraction Algorithm) y MAUI (Multi-Purpose Automatic Topic Indexing) para averiguar cómo funcionan en relación con la indización realzada por especialistas. El algoritmo de SISA se basa en reglas sobre la posición de los términos en los diferentes componentes estructurales del documento, mientras que los algoritmos de KEA y MAUI se basan en el aprendizaje automático y las frecuencia estadística de los términos. Para la evaluación se utilizó una colección documental de 230 artículos científicos de la Revista Española de Documentación Científica, publicada por el Consejo Superior de Investigaciones Científicas (CSIC), de los cuales 30 se utilizaron para tareas formativas y no formaban parte del conjunto de pruebas de evaluación. Los artículos fueron escritos en español e indizados por indizadores humanos utilizando un vocabulario controlado en la base de datos InDICES, también perteneciente al CSIC. La indización humana de estos documentos constituye la referencia contra la cual se evalúa el resultado de los sistemas de indización automáticos, comparando conjuntos de términos usando métricas de evaluación de precisión, recuperación, medida F y consistencia. Los resultados muestran que el sistema SISA funciona mejor, seguido de KEA y MAUI

Open Source Natural Language Processing

Our MQP aimed to introduce finite state machine based techniques for natural language processing into Hunspell, the world\u27s premiere Open Source spell checker used in several prominent projects such as Firefox and Open Office. We created compact machine-readable finite state transducer representations of 26 of the most commonly used languages on Wikipedia. We then created an automata based spell checker. In addition, we implemented an transducer based stemmer, which will be used in the future of transducer based morphological analysis

Open-source resources and standards for Arabic word structure analysis: Fine grained morphological analysis of Arabic text corpora

Morphological analyzers are preprocessors for text analysis. Many Text Analytics applications need them to perform their tasks. The aim of this thesis is to develop standards, tools and resources that widen the scope of Arabic word structure analysis - particularly morphological analysis, to process Arabic text corpora of different domains, formats and genres, of both vowelized and non-vowelized text. We want to morphologically tag our Arabic Corpus, but evaluation of existing morphological analyzers has highlighted shortcomings and shown that more research is required. Tag-assignment is significantly more complex for Arabic than for many languages. The morphological analyzer should add the appropriate linguistic information to each part or morpheme of the word (proclitic, prefix, stem, suffix and enclitic); in effect, instead of a tag for a word, we need a subtag for each part. Very fine-grained distinctions may cause problems for automatic morphosyntactic analysis – particularly probabilistic taggers which require training data, if some words can change grammatical tag depending on function and context; on the other hand, finegrained distinctions may actually help to disambiguate other words in the local context. The SALMA – Tagger is a fine grained morphological analyzer which is mainly depends on linguistic information extracted from traditional Arabic grammar books and prior knowledge broad-coverage lexical resources; the SALMA – ABCLexicon. More fine-grained tag sets may be more appropriate for some tasks. The SALMA –Tag Set is a theory standard for encoding, which captures long-established traditional fine-grained morphological features of Arabic, in a notation format intended to be compact yet transparent. The SALMA – Tagger has been used to lemmatize the 176-million words Arabic Internet Corpus. It has been proposed as a language-engineering toolkit for Arabic lexicography and for phonetically annotating the Qur’an by syllable and primary stress information, as well as, fine-grained morphological tagging

Automatic information retrieval through text-mining

The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaNowadays, around a huge amount of firms in the European Union catalogued as Small and Medium Enterprises (SMEs), employ almost a great portion of the active workforce in Europe. Nonetheless, SMEs cannot afford implementing neither methods nor tools to systematically adapt innovation as a part of their business process. Innovation is the engine to be competitive in the globalized environment, especially in the current socio-economic situation. This thesis provides a platform that when integrated with ExtremeFactories(EF) project, aids SMEs to become more competitive by means of monitoring schedule functionality. In this thesis a text-mining platform that possesses the ability to schedule a gathering information through keywords is presented. In order to develop the platform, several choices concerning the implementation have been made, in the sense that one of them requires particular emphasis is the framework, Apache Lucene Core 2 by supplying an efficient text-mining tool and it is highly used for the purpose of the thesis