Examining research topics with a dependency-based noun phrase extraction method: a case in accounting

PurposeExamining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field.Design/methodology/approachA new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019.FindingsThe study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics.Originality/valueThe research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.

Embedding Additional Natural Language Processing Tools into the TXM Platform

In our work we present a description of integration of natural language processing tools (pseudostem extraction, noun phrase extraction, verb government analysis) in order to extend analytic facilities of the TXM corpora analysis platform. The tools introduced in the paper are combined into a single software package providing TXM platform with an effective specialized corpora preparation tool for further analysis.

A Smart Bot as an Interactive Medical Assistant with Voice-based System using Natural Language Processing

In this fast-moving world, people are ignorant about their health issues and avoid routine check-ups. It is very difficult for users to spend longer time on-line and explore health information. To solve the problem, voice-based application is provided to the user where user can interact with system and get inference of diseases and their remedies by giving the symptoms as input.For processing the given input, the data is normalized by using noun phrase extraction and medical term identifier. For getting more precise result, the system generates relevant questions to the user and accordingly provide remedy for problem. Question is generated by mapping the user input array with question generation matrix. The project is based on a digital medical aid through a smart bot using machine learning and optical character recognition techniques. The user can just talk to the bot and get to know what the possible causes and effects of the particular symptoms are, determine the illness and take appropriate actions. Basically, the purpose of this bot is to act as a friendly healthcare assistant that helps in all the work that people need to take care of their health.