The head-modifier principle and multilingual term extraction

Advances in Language Engineering may be dependent on theoretical principles originating from linguistics since both share a common object of enquiry, natural language structures. We outline an approach to term extraction that rests on theoretical claims about the structure of words. We use the structural properties of compound words to specifically elicit the sets of terms defined by type hierarchies such as hyponymy and meronymy. The theoretical claims revolve around the head-modifier principle which determines the formation of a major class of compounds. Significantly it has been suggested that the principle operates in languages other than English. To demonstrate the extendibility of our approach beyond English, we present a case study of term extraction in Chinese, a language whose written form is the vehicle of communication for over 1.3 billion language users, and therefore has great significance for the development of language engineering technologies

Automatic domain-specific learning: towards a methodology for ontology enrichment

[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C0201.Ureña Gómez-Moreno, P.; Mestre-Mestre, EM. (2017). Automatic domain-specific learning: towards a methodology for ontology enrichment. LFE. Revista de Lenguas para Fines Específicos. 23(2):63-85. http://hdl.handle.net/10251/148357S638523

Hybrid fuzzy multi-objective particle swarm optimization for taxonomy extraction

Ontology learning refers to an automatic extraction of ontology to produce the ontology learning layer cake which consists of five kinds of output: terms, concepts, taxonomy relations, non-taxonomy relations and axioms. Term extraction is a prerequisite for all aspects of ontology learning. It is the automatic mining of complete terms from the input document. Another important part of ontology is taxonomy, or the hierarchy of concepts. It presents a tree view of the ontology and shows the inheritance between subconcepts and superconcepts. In this research, two methods were proposed for improving the performance of the extraction result. The first method uses particle swarm optimization in order to optimize the weights of features. The advantage of particle swarm optimization is that it can calculate and adjust the weight of each feature according to the appropriate value, and here it is used to improve the performance of term and taxonomy extraction. The second method uses a hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems that ensures that the membership functions and fuzzy system rule sets are optimized. The advantage of using a fuzzy system is that the imprecise and uncertain values of feature weights can be tolerated during the extraction process. This method is used to improve the performance of taxonomy extraction. In the term extraction experiment, five extracted features were used for each term from the document. These features were represented by feature vectors consisting of domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. For taxonomy extraction, matching Hearst lexico-syntactic patterns in documents and the web, and hypernym information form WordNet were used as the features that represent each pair of terms from the texts. These two proposed methods are evaluated using a dataset that contains documents about tourism. For term extraction, the proposed method is compared with benchmark algorithms such as Term Frequency Inverse Document Frequency, Weirdness, Glossary Extraction and Term Extractor, using the precision performance evaluation measurement. For taxonomy extraction, the proposed methods are compared with benchmark methods of Feature-based and weighting by Support Vector Machine using the f-measure, precision and recall performance evaluation measurements. For the first method, the experiment results concluded that implementing particle swarm optimization in order to optimize the feature weights in terms and taxonomy extraction leads to improved accuracy of extraction result compared to the benchmark algorithms. For the second method, the results concluded that the hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems leads to improved performance of taxonomy extraction results when compared to the benchmark methods, while adjusting the fuzzy membership function and keeping the number of fuzzy rules to a minimum number with a high degree of accuracy

Corpus and sentiment analysis

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Ontology Learning from the Arabic Text of the Qur’an: Concepts Identification and Hierarchical Relationships Extraction

Recent developments in ontology learning have highlighted the growing role ontologies play in linguistic and computational research areas such as language teaching and natural language processing. The ever-growing availability of annotations for the Qur’an text has made the acquisition of the ontological knowledge promising. However, the availability of resources and tools for Arabic ontology is not comparable with other languages. Manual ontology development is labour-intensive, time-consuming and it requires knowledge and skills of domain experts. This thesis aims to develop new methods for Ontology learning from the Arabic text of the Qur’an, including concepts identification and hierarchical relationships extraction. The thesis presents a methodology for reducing human intervention in building ontology from Classical Arabic Language of the Qur’an text. The set of concepts, which is a crucial step in ontology learning, was generated based on a set of patterns made of lexical and inflectional information. The concepts were identified based on adapted weighting schema that exploit a combination of knowledge to learn the relevance degree of a term. Statistical, domain-specific knowledge and internal information of Multi-Word Terms (MWTs) were combined to learn the relevance of generated terms. This methodology which represents the major contribution of the thesis was experimentally investigated using different terms generation methods. As a result, we provided the Arabic Qur’anic Terms (AQT) as a training resource for machine learning based term extraction. This thesis also introduces a new approach for hierarchical relations extraction from Arabic text of the Qur’an. A set of hierarchical relations occurring between identified concepts are extracted based on hybrid methods including head-modifier, set of markers for copula construct in Arabic text, referents. We also compared a number of ontology alignment methods for matching ontological bilingual Qur’anic resources. In addition, a multi-dimensional resource named Arabic Qur’anic Database (AQD) about the Qur’an is made for Arabic computational researchers, allowing regular expression query search over the included annotations. The search tool was successfully applied to find instances for a given complex rule made of different combined resources