Computational Linguistic

South Africa has eleven official languages. However, not all have received similar amounts of attention. In particular, for many of the languages, only a limited number of digital language resources (data sets and computational tools) exist. This scarcity hinders (computational) research in the fields of humanities and social sciences for these languages. Additionally, using existing computational linguistics tools in a practical setting requires expert knowledge on the usage of these tools. In South Africa, only a small number of people currently have this expertise, further limiting the type of research that relies on computational linguistic tools. The South African Centre for Digital Language Resources (SADiLaR) aims to enable and enhance research in the area of language technology by focusing on the development, management, and distribution of digital language resources for all South African languages. Additionally, it aims to build research capacity, specifically in the field of digital humanities. This requires several challenges to be resolved that we cluster under resources, training, and community building. SADiLaR hosts a repository of existing digital language resources and supports the development of new resources. Additionally, it provides training on the use of these resources, specifically for (but not limited to) researchers in the fields of humanities and social sciences. Through this training, SADiLaR tries to build a community of practice to boost information sharing in the area of digital humanities.

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Definition extraction from court decisions using computational linguistic technology

Formal Linguistics and Law - Trends in Linguistics. Studies and Monographs [TiLSM] ◽

10.1515/9783110218398.3.183 ◽

2009 ◽

Keyword(s):

Computational Linguistic ◽

Court Decisions

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The Term “Global” in Cross-Cultural Studies

Transcultural Marketing for Incremental and Radical Innovation - Advances in Marketing, Customer Relationship Management, and E-Services ◽

10.4018/978-1-4666-4749-7.ch009 ◽

2014 ◽

pp. 166-208

Author(s):

Bice Della Piana ◽

Mario Monteleone

Keyword(s):

Computational Linguistic ◽

Cultural Studies ◽

Textual Analysis ◽

Cross Cultural ◽

Cultural Management ◽

Cultural Research ◽

Cross Cultural Research ◽

The World ◽

Cross Cultural Management ◽

Cross Cultural Studies

Globalization in its broadest sense is part of the strongest external environmental forces that affect organisations today (Daniels, Radebaugh, & Sullivan, 2011). Despite the “slogan” that globalization is likely to produce homogeneity in various cultures, different civilizations of the world will reassert themselves to preserve their cultural heritages. This means it is necessary to consider the dynamics of the continued interplay between various trends in world cultures and the process of globalization. Moving from these considerations, cross-cultural management research seems to be the natural framework for using the term globalization. Cross-cultural research has received considerable attention by management scholars. The purpose of this study is to understand the connotation of the term “global” used in the context of cross-cultural studies and the enrichment of his meaning in the last four decades. To achieve it, the authors use a computational linguistic tool, namely an automatic textual analysis software, by means of which they locate and extract specific linguistic expressions. This allows retrieval and location of a series of concepts that denote and connote the term “global” over the last four decades.

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Exploring “Mass Surveillance” Through Computational Linguistic Analysis of Five Text Corpora

Techniques for Coding Imagery and Multimedia - Advances in Knowledge Acquisition, Transfer, and Management ◽

10.4018/978-1-5225-2679-7.ch004 ◽

2017 ◽

pp. 212-286

Keyword(s):

Computational Linguistic ◽

Linguistic Features ◽

Text Corpora ◽

Government Documents ◽

Linguistic Inquiry ◽

The Government ◽

Digital Ink ◽

The One ◽

Mass Surveillance ◽

Shed Light

A lot of digital ink has been spilled on the issue of “mass surveillance,” in the aftermath of the Edward Snowden mass data leak of secret government communications intelligence (COMINT) documents in 2013. To explore some of the extant ideas, five text sets were collected: academic articles, mainstream journalistic articles, Twitter microblogging messages from a #surveillance hashtag network, Wikipedia articles in the one-degree “Mass_surveillance” page network, and curated original leaked government documents. These respective text sets were analyzed with Linguistic Inquiry and Word Count (LIWC) (by Pennebaker Conglomerates, Inc.) and NVivo 11 Plus (by QSR International, Inc.). Also, the text sets were analyzed through close (human) reading (except for the government documents that were treated in a non-consumptive way). Using computational text analytics, this author found text patterns within and across the five text sets that shed light on the target topic. There were also discoveries on how textual conventions affect linguistic features and informational contents.

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Using LIWC and Coh-Metrix to Investigate Gender Differences in Linguistic Styles

Applied Natural Language Processing ◽

10.4018/978-1-60960-741-8.ch032 ◽

2012 ◽

pp. 545-556 ◽

Cited By ~ 4

Author(s):

Courtney M. Bell ◽

Philip M. McCarthy ◽

Danielle S. McNamara

Keyword(s):

Gender Differences ◽

Computational Linguistic ◽

Marital Conflict ◽

Latent Semantic Analysis ◽

Biological Theory ◽

Semantic Analysis ◽

Negative Emotion ◽

Language Differences ◽

Biological Sex ◽

Logical Connectors

We use computational linguistic tools to investigate gender differences in language use within the context of marital conflict. Using the Language Inquiry and Word Count tool (LIWC), differences between genders were significant for the use of self references, but not for the use of social words and positive and negative emotion words. Using Coh-Metrix, differences were significant for the use of syntactic complexity, global argument overlap, and density of logical connectors but not for the use of word frequency, frequency of causal verbs and particles, global Latent Semantic Analysis (LSA), local argument overlap, and local LSA. These results confirmed some expectations but failed to confirm the majority of the expectations based on the biological theory of gender, which defines gender in terms of biological sex resulting in polarized and static language differences based on the speaker’s gender.

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