Sentence examples for lexical data from inspiring English sources

We present many experimental data obtained from different corpora in different domains and languages, and show that the acquired lexical data not only have practical applications in NLP, but they are indeed useful for a comparative analysis of sublanguages.

In a recent paper, Lee and Hasegawa (2011) attempt to overcome this and other defects of glottochronological approaches using a Bayesian phylogenetic analysis based on lexical data from 59 Japonic varieties.

We recently used computational phylogenetic methods on lexical data to test between two scenarios for the peopling of the Pacific.

Recently we tested between scenarios of Pacific settlement by applying Bayesian phylogenetic methods to lexical data [3].

The past few years have seen a number of high-profile applications of Bayesian phylogenetic methods to lexical data [1], [2], [3] that have been very controversial [4].

The lexical data we used to test these hypotheses was drawn from the Austronesian Basic Vocabulary Database [21] which contains wordlists of 210 items of basic vocabulary that are thought to be stable over time and resistant to borrowing such as words for body parts, animals, kinship terms, simple verbs, colors, and numbers [21].

These correlations, based on measurements collected by Cortese and Khanna (2007, 2008) are listed in Table  6 and clearly show the overwhelming similarity between the Balota et al. (2004) lexical decision data set and the BLP data.

Lexical decision data were analyzed for speed and accuracy, both by participants and by items.

Lexical decision data were analyzed for speed and accuracy, as in Experiment 1a.

In English and French, it has also been found that word frequencies based on written texts explain a few percentages of extra variance in visual lexical decision data to those based on film subtitles, even though the written frequencies themselves are inferior to the subtitle frequencies.

We then test the different explanations by fitting the model to the lexical decision data from Keuleers, Brysbaert, & New (2010).