Bushra Siddique

A Reverse Dictionary maps a natural language description to corresponding semantically appropriate words. It is of assistance, particularly to the language producers, in finding the correct word for a concept in mind while writing/speaking. As the COVID-19 pandemic intensely impacted almost all the functionalities across the globe, texts on this subject appear in a significant amount in various forms, including news updates, awareness and safety articles, notices and circulars, research articles, social media posts, etc. A Reverse Dictionary on this subject is a requisite in view of the following reasons, hence addressed. Firstly, the varied text forms involve a diverse range of language producers ranging from professional doctors to the general mass. Secondly, the COVID-19 pandemic’s glossary is more specific than the general English language, hence unfamiliar to the language producers. We have carried out an implementation based on the Wordster Reverse Dictionary architecture, owing to its outperformance of the commercial Onelook Reverse Dictionary benchmark. We report an accuracy of 0.49 based on top-3 system responses. To address the limitations of the current implementation, we bring into consideration Zadeh’s paradigm of the Computational Theory of Perceptions. Notably, the compilation of the COVID-19 glossary as a part of this study is another contribution in view that it is of assistance to the concerned readers.

Owing to the large information content of adjectives in natural language-based data, we have dealt with the subject of adjective phrases in the paradigm of PNL. We have proposed a GCR form for the same and demonstrated its application in building a specific instance of Reverse Dictionary. A Reverse Dictionary takes a natural language description of a concept in the user’s mind as input and generates semantically appropriate word/s as output. For example, it could accept descriptions like ‘to run slowly,’ ‘to run but not very fast,’ etc., for the word ‘jog’. As Reverse Dictionary deals with natural language, handling perception-based information is important in its context for capturing the user intent. The D-RD, Reverse Dictionary for Descriptive words, designed in this paper, takes a natural language description of a human as input and generates appropriate adjectives for usage in text/speech as output. As a part of the design, we have proposed a novel CWW based similarity measure for calculating semantic similarity between adjective phrases. The D-RD is evaluated against two benchmarks, Onelook.com and WantWords Online RD, and is reported to outperform both for the test set under consideration.

In the near past, microblogging services like Twitter have gained immense popularity. The vast breadth of user base is responsible for generating information on diverse aspects ranging from product launch to sports match. However, due to the exponentially increasing number of users on Twitter platform, the volume of content generated is tremendously high. In this paper we address the information overload problem of the Twitter and present a framework for event detection with hierarchical visualization specifically for sports events. We propose a novel Event Tree algorithm which detects and generates a hierarchy of events through recursive hierarchical clustering. The different levels of the hierarchy represent the events at different granularities of time and thus offer dual advantages. Firstly, it takes care of the users with varied level of interest in the particular sports event. Secondly, the users may get finer details for specific segments of the sport holdings as per their appeal. We test and report results of our framework for the Indian Premier League Twenty20 2016 season cricket match dataset.