A method for constructing word sense embeddings based on word sense induction.

Polysemy is an inherent characteristic of natural language. In order to make it easier to distinguish between different senses of polysemous words, we propose a method for encoding multiple different senses of polysemous words using a single vector. The method first uses a two-layer bidirectional long short-term memory neural network and a self-attention mechanism to extract the contextual information of polysemous words. Then, a K-means algorithm, which is improved by optimizing the density peaks clustering algorithm based on cosine similarity, is applied to perform word sense induction on the contextual information of polysemous words. Finally, the method constructs the corresponding word sense embedded representations of the polysemous words. The results of the experiments demonstrate that the proposed method produces better word sense induction than Euclidean distance, Pearson correlation, and KL-divergence and more accurate word sense embeddings than mean shift, DBSCAN, spectral clustering, and agglomerative clustering.

Population data mobility retrieval at territory of Czechia in pandemic COVID-19 period.

This article describes the methodology and the possibilities of collecting operation data in a mobile network provider. First, the architecture and the principles used in the system are described. The precision analysis of the population commuting in the region and during the pandemic and nonpandemic times. Moreover, several ideas about further utilization of the data will be formulated and described. Finally, a graph-based approach that describes the creation of the community structure between the people and the means of its analysis.

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Statistical-based system combination approach to gain advantages over different machine translation systems.

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models.

Closed trail distance in a biconnected graph.

Graphs describe and represent many complex structures in the field of social networks, biological, chemical, industrial and transport systems, and others. These graphs are not only connected but often also k-connected (or at least part of them). Different metrics are used to determine the distance between two nodes in the graph. In this article, we propose a novel metric that takes into account the higher degree of connectivity on the part of the graph (for example, biconnected fullerene graphs and fulleroids). Designed metric reflects the cyclical interdependencies among the nodes of the graph. Moreover, a new component model is derived, and the examples of various types of graphs are presented.