ComputônMixedR

ComputônMixedR is a computational linguistics research project focused on developing advanced methods for analyzing and understanding mixed-language data. The project's primary objective is to create robust models capable of processing and interpreting text or speech that combines elements from multiple languages. This includes phenomena such as code-switching, code-mixing, and language borrowing.

The research within ComputônMixedR encompasses several key areas. One focus is on developing novel tokenization strategies that can effectively segment and identify words and phrases from different linguistic sources within a single utterance. Another significant area is the creation of language identification models specifically trained to handle the nuances of mixed-language inputs, often outperforming traditional monolingual models. Furthermore, ComputônMixedR investigates the application of deep learning architectures, including transformer-based models, to tasks such as sentiment analysis, machine translation, and named entity recognition on mixed-language corpora.

The project aims to contribute to the fields of natural language processing (NLP) and computational social science by providing tools and techniques that better reflect the linguistic realities of multilingual communities. The outputs of ComputônMixedR are expected to have applications in areas like cross-lingual information retrieval, educational technology for bilingual learners, and the analysis of social media conversations. The project emphasizes the importance of handling linguistic diversity and aims to build more inclusive and accurate NLP systems.