A Novel Sentence Completion System for Punjabi using Deep Neural Networks

Sentence completion systems are actively studied by many researchers which ultimately results in the reduction of cognitive effort and enhancement in user-experience. The review of the literature reveals that most of the work in the said area is in English and limited effort spent on other languages, especially vernacular languages. This work aims to develop state-of-the-art sentence completion system for the Punjabi language, which is the 10th most spoken language in the world. The presented work is an outcome of the results of the experimentation on various neural network language model combinations. A new Sentence Search Algorithm (SSA) and patching system are developed to search, complete and rank the completed sub-string and give a syntactically rich sentence(s). The quantitative and qualitative evaluation metrics were utilized to evaluate the system. The results are quite promising, and the best performing model is capable of completing a given sub-string with more acceptability. Best performing model is utilized for developing the user-interface.

Technology of training a neural-network model for ontology population

The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

Training set augmentation in training neural- network language model for ontology population

This paper is a continuation of the research focused on solving the problem of ontology population using training on an automatically generated training set and the subsequent use of a neural-network language model for analyzing texts in order to discover new concepts to add to the ontology. The article is devoted to the text data augmentation - increasing the size of the training set by modification of its samples. Along with this, a solution to the problem of clarifying concepts (i.e. adjusting their boundaries in sentences), which were found during the automatic formation of the training set, is considered. A brief overview of existing approaches to text data augmentation, as well as approaches to extracting so-called nested named entities (nested NER), is presented. A procedure is proposed for clarifying the boundaries of the discovered concepts of the training set and its augmentation for subsequent training a neural-network language model in order to identify new concepts of ontology in the domain texts. The results of the experimental evaluation of the trained model and the main directions of further research are considered.

On the Formation of the New Construction “ta bu xiang ma” of Polysemy Isomorphism and Its Usage in Discourse

The paper takes “ta bu xiang ma2” as the research object, and analyzes its formation, function, and application in discourse. The study results indicate that “ta bu xiang ma2” is a new and fillable construction that expresses the meaning of the domain of knowing and uttering. The structure has the functions of subjectivity, exclamation, anticipation and illocutionary. The formation mechanism is metaphor, and the motivation is the widespread use of network language and people’s psychology of seeking novelty and differences. “Ta bu xiang ma2” has strong spoken language style and is used in interactive and evaluative contexts. It can be used as both trigger and response sentences, and it has similarities and differences with its related formats.