Improve Hamming character difference based-on derivative lexical similarity and right space padding

Hamming character difference represents one of the most common problems that can be occurred when students try to answer questions of fill in the gaps that need mostly to one word as the answer. To improve the evaluation of the student answer using Hamming distance, our proposed Hamming model tried to solve the drawbacks of the standard Hamming model by applying a stemming approach to achieve derivative lexical similarity and applying right space padding to deal with unequal lengths of the texts.

Patterns of semantic variation differ across body parts: evidence from the Japonic languages

The human body is central to myriad metaphors, so studying the conceptualisation of the body itself is critical if we are to understand its broader use. One essential but understudied issue is whether languages differ in which body parts they single out for naming. This paper takes a multi-method approach to investigate body part nomenclature within a single language family. Using both a naming task (Study 1) and colouring-in task (Study 2) to collect data from six Japonic languages, we found that lexical similarity for body part terminology was notably differentiated within Japonic, and similar variation was evident in semantics too. Novel application of cluster analysis on naming data revealed a relatively flat hierarchical structure for parts of the face, whereas parts of the body were organised with deeper hierarchical structure. The colouring data revealed that bounded parts show more stability across languages than unbounded parts. Overall, the data reveal there is not a single universal conceptualisation of the body as is often assumed, and that in-depth, multi-method explorations of under-studied languages are urgently required.

Exploring the Effect of Conversion on the Distribution of Inflectional Suffixes: A Multivariate Corpus Study

Lexical ambiguity in the English language is abundant. Word-class ambiguity is even inherently tied to the productive process of conversion. Most lexemes are rather flexible when it comes to word class, which is facilitated by the minimal morphology that English has preserved. This study takes a multivariate quantitative approach to examine potential patterns that arise in a lexicon where verb-noun and noun-verb conversion are pervasive. The distributions of three inflectional suffixes, verbal -s, nominal -s, and -ed are explored for their interaction with degrees of verb-noun conversion. In order to achieve that, the lexical dispersion, context-dependency, and lexical similarity between the inflected and bare forms were taken into consideration and controlled for in a Generalized Additive Models for Location, Scale and Shape (GAMLSS; Stasinopoulos, M. D., R. A. Rigby, and F. De Bastiani. 2018. “GAMLSS: A Distributional Regression Approach.” Statistical Modelling 18 (3–4): 248–73). The results of a series of zero-one-inflated beta models suggest that there is a clear “uncanny” valley of lexemes that show similar proportions of verbal and nominal uses. Such lexemes have a lower proportion of inflectional uses when textual dispersion and context-dependency are controlled for. Furthermore, as soon as there is some degree of conversion, the probability that a lexeme is always encountered without inflection sharply rises. Disambiguation by means of inflection is unlikely to play a uniform role depending on the inflectional distribution of a lexeme.

Making Visible the Invisible Work of Scientists during the COVID-19 Pandemic

Despite the perceptibility of the effects they impart on their hosts, the most incredible capacity of viruses is in their invisibility. Invisibility is the most frightening side of the current pandemic, and invisible is also the work of the scientists striving to find a solution.This proposal presents a data visualization that aims to give visibility to those scientists working on COVID-19. Their scientific publications have been computationally analyzed and transformed into a relational structure based on lexical similarity. The result is a network of scientists whose proximity is given by their closeness in writing.An innovative visual method that hybridizes network visualizations and word clouds shows the scientists in a deep space, explorable through keywords. In such a space, individuals are situated according to their lexical similarity, and keywords are used to clarify their proximity. By zooming, the visualization reveals more information about scientists and their clusters.While a lot of visualizations during the pandemic focused on showing the spread of infection, causing anxiety among the readers, this visualization reveals the efforts of science in eradicating the virus. Making visible the enormous number of scientists working on COVID-19 research will contribute to coping more positively with the pandemic.

Ethno-Linguistic Vitality of Koch

The Koch language is spoken in the states of Assam (Goalpara, Nagaon, Dhubri, Kokrajhar, Chirang, Bongaigao, Barpeta, Baksa, Udalguri, Karbi Anglong, Golaghat districts), Meghalaya (West Garo Hills, South-West Garo Hills, South Garo Hills and East Khasi Hills Districts). Koches are found in West Bengal (Northern part) and also in Bangladesh. The speaker strength of Koch in India according to 2011 census is 36,434. Koch community is the bilingual speakers of Assamese, Bengali, Garo, Hindi, and English. Contact situations of Koch with Assamese and Bengali languages have made the language vulnerable to language shift. The UNESCO report mentions Koch as ‘Definitely Endangered’1. Koch has gained the status of a scheduled tribe in Meghalaya in 1987. Kondakov (2013) traces six distinct dialects of Koch, viz., Wanang, Koch-Rabha (Kocha), Harigaya, Margan, Chapra and Tintekiya. He (2013:24) states, “The relationship between the six Koch speech varieties are rather complex. They represent a dialect chain that stretches out from Koch-Rabha in the north to Tintekiya Koch in the south.” This is diagrammatically represented as - Koch-Rabha(Kocha)→Wanang→Harigaya→Margan, Chapra→Tintekiya where the adjacent dialects exhibit more lexical similarity than those at the ends. Nine ethno-linguistic varieties of Koch (also mentioned in Kondakov, 2013:5) have been reported during field investigation. These are Harigaya, Wanang, Tintekiya, Margan, Chapra, Satpariya, Sankar, Banai and Koch Mandai.