Multidimensional Analysis Tagger Download
Cynthia Figarsky
MulDi Chinese (IPA: [ˌmʌl'daɪ] [ˌtʃaɪˈniːz]) is a multidimensional analysis tagger of Mandarin Chinese.
- Installation: pip install muldichinese
The purpose of this study is to investigate how genres vary and figure out the factors that generate genre variation. The quantitative multi-dimensional analysis is used to examine genre variation of cross-border e-commerce English in South Asia. The texts in the observed corpus collected from four country websites of Daraz, a significant cross-border e-commerce platform, were tagged and analyzed using Multidimensional Analysis Tagger and SPSS statistical software. The results of linear regression analysis, independent sample t-test and Analysis of Variance show salient differences between the observed corpus and reference corpus. The research also indicates that four sub-corpora from country Websites are brought into line with each other. They show salient differences (p
The exponential growth in the population of Chinese EFL learners has fueled the study of Chinese EFL learner writing. A survey of relevant literature indicates that the majority of studies are confined to the exploration of individual linguistic features, with a few exceptions which employ a broader perspective that might involve multiple features. This work aims to investigate the English writings by Chinese EFL learners via Multi-Dimensional (MD) analysis, a corpus-based approach that combines both microscopic (i.e., individual linguistic features) and macroscopic perspectives (i.e., textual dimensions). A comparison between writings by Chinese EFL learners and native English speakers shows that the former are high on involvement, informativeness, and referential explicitness while the latter exhibit superiority on word-choosing, information integration, narrativity, and persuasiveness. Regarding their specific use of 67 MD linguistic features, the two writer groups also show certain significant but interesting differences. Analysis of Chinese EFL learner corpora from different English education levels indicates that writings by learners from higher levels are lower on involvement, but are higher on informativeness, narrativity, referential explicitness, and persuasiveness. This trend is manifested by their decreasing use of involvement features, but increasing use of features marking the latter four aspects.
Wen, Q. F., Ding, Y. R., & Wang, W. (2003). Zhong guo da xue sheng ying yu shu mian yu zhong de kou yu hua qing xiang: gao shui ping ying yu xue xi zhe yu liao dui bi fen xi [Features of oral style in English compositions of advanced Chinese EFL learners: An exploratory study by contrastive learner corpus analysis]. Wai yu jiao xue yu yan jiu, 4, 268-274.
Using the multidimensional analysis conducted on a corpus of 40 texts, this positivist study investigates the linguistic variation between philosophical fiction and non-fiction prose writings based on the scores of five dimensions calculated by MAT software. It uses the functional mapping of linguistic items to justify the differences in the dimension scores of both categories. The study finds that score difference on dimension three named explicit versus situation-dependent is quite significant because philosophical non-fiction is highly explicit in references. It hints at the independent status of the two types of writing as sub-genres of the philosophical prose.
Given the great significance of diplomatic discourse to not only diplomacy but also national development, studies of diplomatic discourse have become increasingly interdisciplinary and have drawn the attention of scholars from such various fields as linguistics, translation studies, communication, international studies, politics, and sociology (Hu and Tian, 2018; Liang, 2019; Tungkeunkunt and Phuphakdi, 2018; Yang and Zhou, 2020; Liu and Afzaal, 2021). However, studies of linguistic features of diplomatic discourse mainly focus on the relative distribution of linguistic features considered individually, despite the growing evidence that sets of co-occurring features can better reveal the underlying structure of textual variation (Biber, 1988, 2006). Therefore, a systemic analysis of the co-occurring linguistic features in the diplomatic discourse of China and the United States will offer valuable insights into how linguistic practices play a role in diplomacy.
Many scholars examine the disseminative effects of diplomatic discourse to explore the relationship between diplomatic discourse and international relations (Tungkeunkunt and Phuphakdi, 2018). However, there are several noteworthy gaps in the existing literature. First, despite the growing popularity of corpus tools in linguistics, they are rarely used to analyze the linguistic features of diplomatic discourse. The corpus approach, which provides many authentic linguistic resources, is a valuable way to improve the representativeness and systematicness of linguistic features of diplomatic discourse studies. Second, studies have yet to compare linguistic variation in the diplomatic discourse of different countries. The way to gain more support in international affairs and earn a favorable reputation among international audiences can be further improved by comparing the linguistic features of the diplomatic discourse of different countries. Third, given the prominence of the discourse analysis perspective and the corpus-based approach in studies of the linguistic features of diplomatic discourse, MD analysis is well-suited for this type of research. Nevertheless, few existing studies have used this approach.
This study presents a cross-cultural investigation of linguistic variations in English argumentative essays authored by Pakistani and Chinese learners, employing a multidimensional analysis (MDA) approach following Biber's framework (1988). The corpus, composed of 400 essays from Pakistani and Chinese learners, was sourced from the online repository ICNALE, electronically processed, tagged, and analyzed using the MAT tagger. Focusing on five dimensions, this study investigates the comparative linguistic characteristics that distinguish these two groups of learners in the context of argumentative essay writing. It is essential to note that this research centers on learners from Pakistan and China, specifically within the sub-register of argumentative essays. The findings reveal that Pakistani learners' essays demonstrate an informational style, while those of their Chinese counterparts exhibit narrative elements on Dimension 1. Moreover, Chinese learners' essays embrace a more context-dependent and nominalization-rich approach on Dimension 3, whereas Pakistani essays prioritize independence from context. Chinese essays are characterized by explicitness and persuasive language, including extensive use of modal verbs on Dimension 4, while Pakistani essays exhibit a more concrete and objective quality, maintaining a natural tone. Finally, Chinese essays exhibit a formal, technical, and abstract style, in contrast to the non-abstract, objective, and natural tone found in Pakistani essays on Dimension 5. In conclusion, this study highlights how learners acquire and utilize vocabulary and expressions from diverse texts, influencing various lexical and grammatical aspects that shape their distinct linguistic styles. The MD analysis identifies specific linguistic features that are either over- or under-utilized, thus contributing to a richer understanding of cross-cultural variations in argumentative essays.
This measurement is a 2-dimensional version of the Histogram measurement.The measurement accumulates a two-dimensional histogram where stop signals from twoseparate channels define the bin coordinate. For instance, this kind of measurementis similar to that of typical 2D NMR spectroscopy.The data within the histogram is acquired via a single-start, single-stop analysis for each axis.The first stop click of each axis is taken after the start click to evaluate the histogram counts.
This measurement is the generalisation of Histogram2D to an arbitrary number of dimensions.The data within the histogram is acquired via a single-start, single-stop analysis for each axis.The first stop click of each axis is taken after the start click to evaluate the histogram counts.
Returns a one-dimensional array of the size of the product of n_bins containing the histogram data. The array order is in row-major. For example, with stop_channels=[ch1, ch2] and n_bins=[2, 2], the 1D array would represent 2D bin indices in the order [(0,0), (0,1), (1,0), (1,1)], with (index of ch1, index of ch2). Please reshape the 1D array to get the N-dimensional array. The following code demonstrates how to reshape the returned 1D array into multidimensional array using NumPy.
Returns a proxy tagger object which can be passed to the constructor of a measurement class to register the measurements at initialization to the synchronized measurement object. Those measurements will not start automatically.
The proxy tagger object returned by getTagger() is not identical with the TimeTagger object created by createTimeTagger(). You can create synchronized measurements with the proxy object the following way:
The constructor of the CustomMeasurement class itself takes only the parameter tagger. When you sub-class your own measurement,you can add to your constructor any parameters that are necessary for your measurement. You can find detailed examples in your example folder.
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