This bibliometric study aims to map the conceptual structure of intellectual capital (IC) research between 1975 and 2020 using co-word analysis social network drawing upon Web Science database. The results show that 12,310 documents have been published from 2020. From a total 6,516 keywords used in documents, five most frequent identified as: “performance”, “innovation”, “knowledge”, “impact”, ...

We employ hierarchical clustering, strategic diagrams, and network core–periphery analysis to assess visualise the intellectual progress of accessibility research within HCI in past two decades. The study quantifies explains development its thematic evolution based on 1,535 papers published at TACCESS, ASSETS, IJHCS, CHI their respective 3,470 author-assigned keywords. novelty this work is empl...

To alleviate the data sparseness problem during word alignment, we propose a word alignment method based on word co-occurrence degree. In this paper, we propose a new method to get the statistical information from word cooccurrence. We combine the co-occurrence counts and the fuzzy co-occurrence weights as word co-occurrence degree. Fuzzy co-occurrence weights can be obtained by searching for f...

A growing body of research on early word learning suggests that learners gather word-object co-occurrence statistics across learning situations. Here we test a new mechanism whereby learners are also sensitive to word-word co-occurrence statistics. Indeed, we find that participants can infer the likely referent of a novel word based on its co-occurrence with other words, in a way that mimics a ...

Computational creativity is one area of NLP which requires extensive analysis of large datasets. Laalalaa [1] framework for Lyric analysis and generation proposed a lyric analysis subsystem that required statistical analysis of Tamil lyrics. In this paper, we propose a data analysis model for words, rhymes and their usage in Tamil lyrics. The proposed analysis model extracts the root words from...

Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic maps. Chinese characters contain not only information, but also meaning. This may enhan...