Identification of discursive topics based on textual similarity
Keywords:
spoken language, computational linguistics, linguistic research, speechAbstract
A major outcome of linguistic research is the characterization of a community based on observations that, supported by different specialized areas, such as field linguistics, sociolinguistics, ethnolinguistics, and even corpus linguistics, summarize the most representative features of such communities. Most of these features are based on descriptive statistics, in which frequency plays a relevant role: The higher the frequency of a feature, the more representative it will be. In this regard, it is undeniable that any statistical representation represents a global overview of the lexical generalities of a community; however, frequency is not an absolute in terms of its linguistic particularities. In this respect, this study addresses the identification of discursive topics from the recognition of semantic and discursive patterns, which are not necessarily related to a higher statistical frequency. To this end, a vectorial representation process is described. This is based on the application of textual similarity metrics to automatically recognize the underlying topics regarding the interviews stored in the Corpus del Habla de Baja California. The results show a set of topics to characterize, both explicitly and, to a lesser extent, implicitly, the discursive interests of the speakers of the Baja Californian cities of Mexicali, Tijuana, and Ensenada.
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