Acquiring Domain-Specific Knowledge for WordNet from a Terminological Database

In this research we explore a terminological database (Termoteca) in order to expand the Portuguese and Galician wordnets (PULO and Galnet) with the addition of new synset variants (word forms for a concept), usage examples for the variants, and synset glosses or definitions. The methodology applied in this experiment is based on the alignment between concepts of WordNet (synsets) and concepts described in Termoteca (terminological records), taking into account the lexical forms in both resources, their morphological category and their knowledge domains, using the information provided by the WordNet Domains Hierarchy and the Termoteca field domains to reduce the incidence of polysemy and homography in the results of the experiment. The results obtained confirm our hypothesis that the combined use of the semantic domain information included in both resources makes it possible to minimise the problem of lexical ambiguity and to obtain a very acceptable index of precision in terminological information extraction tasks, attaining a precision above 89% when there are two or more different languages sharing at least one lexical form between the synset in Galnet and the Termoteca record

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field

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\%

An ontology for human-like interaction systems

This report proposes and describes the development of a Ph.D. Thesis aimed at building an ontological knowledge model supporting Human-Like Interaction systems. The main function of such knowledge model in a human-like interaction system is to unify the representation of each concept, relating it to the appropriate terms, as well as to other concepts with which it shares semantic relations. When developing human-like interactive systems, the inclusion of an ontological module can be valuable for both supporting interaction between participants and enabling accurate cooperation of the diverse components of such an interaction system. On one hand, during human communication, the relation between cognition and messages relies in formalization of concepts, linked to terms (or words) in a language that will enable its utterance (at the expressive layer). Moreover, each participant has a unique conceptualization (ontology), different from other individual’s. Through interaction, is the intersection of both part’s conceptualization what enables communication. Therefore, for human-like interaction is crucial to have a strong conceptualization, backed by a vast net of terms linked to its concepts, and the ability of mapping it with any interlocutor’s ontology to support denotation. On the other hand, the diverse knowledge models comprising a human-like interaction system (situation model, user model, dialogue model, etc.) and its interface components (natural language processor, voice recognizer, gesture processor, etc.) will be continuously exchanging information during their operation. It is also required for them to share a solid base of references to concepts, providing consistency, completeness and quality to their processing. Besides, humans usually handle a certain range of similar concepts they can use when building messages. The subject of similarity has been and continues to be widely studied in the fields and literature of computer science, psychology and sociolinguistics. Good similarity measures are necessary for several techniques from these fields such as information retrieval, clustering, data-mining, sense disambiguation, ontology translation and automatic schema matching. Furthermore, the ontological component should also be able to perform certain inferential processes, such as the calculation of semantic similarity between concepts. The principal benefit gained from this procedure is the ability to substitute one concept for another based on a calculation of the similarity of the two, given specific circumstances. From the human’s perspective, the procedure enables referring to a given concept in cases where the interlocutor either does not know the term(s) initially applied to refer that concept, or does not know the concept itself. In the first case, the use of synonyms can do, while in the second one it will be necessary to refer the concept from some other similar (semantically-related) concepts...Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaSecretario: Inés María Galván León.- Secretario: José María Cavero Barca.- Vocal: Yolanda García Rui

Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit

The primary focus of this thesis is to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis. The first task, Sanskrit Word Segmentation (SWS), is a fundamental text processing task for any other downstream applications. However, it is challenging due to the sandhi phenomenon that modifies characters at word boundaries. Similarly, the existing dependency parsing approaches struggle with morphologically rich and low-resource languages like Sanskrit. Compound type identification is also challenging for Sanskrit due to the context-sensitive semantic relation between components. All these challenges result in sub-optimal performance in NLP applications like question answering and machine translation. Finally, Sanskrit poetry has not been extensively studied in computational linguistics. While addressing these challenges, this thesis makes various contributions: (1) The thesis proposes linguistically-informed neural architectures for these tasks. (2) We showcase the interpretability and multilingual extension of the proposed systems. (3) Our proposed systems report state-of-the-art performance. (4) Finally, we present a neural toolkit named SanskritShala, a web-based application that provides real-time analysis of input for various NLP tasks. Overall, this thesis contributes to making Sanskrit manuscripts more accessible by developing robust NLP technology and releasing various resources, datasets, and web-based toolkit.Comment: Ph.D. dissertatio