Developing Assamese Information Retrieval System Considering NLP Techniques: an attempt for a low resourced language

This paper engulfs the activities involved in developing a Monolingual Information Retrieval (IR) system for an Indo-Aryan language- Assamese. In a multilingual country like India, where 23 official languages exist, the task of digitizing local language contents is growing tremendously. To meet the need of each individual’s relevant information, monolingual Information Retrieval in own language is very essential. The work aims to develop a search engine that retrieves relevant information for the fired query in one's respective language. Various Linguists, Researchers collaborated with the work, provided valuable information and developed various important resources. Many informative resources, language resources, tools technologies were research, analyze, develop and applied in implementing the overall pipeline. The search engine is frame worked on open search platforms- Solr and Nutch with NLP applications embedded in it. Computational Linguistics or Natural Language Processing (NLP) enhances the performance of the IR system. Each phase of the system is being elaborately described in this paper and explained step-wise. This work is a remarkable contribution to Assamese language technology and an important application of NLP

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