Program type:

Graduate Academic Certificate

On Campus
Est. time to complete:

1 year
Credit Hours:

Develop in-demand language processing skills to solve human-computer interaction challenges with language processing technology.
Computational Linguistics (CL) and Natural Language Processing (NLP) - often viewed as one and the same thing - are rapidly growing fields in the high-tech sector, critical for the development of such technologies such as voice-enabled smartphones (e.g. Siri, Cortana) and home assistants (e.g. Alexa).

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Why Earn a Computational Linguistics Certificate?

UNT's Computational Linguistics graduate academic certificate is specifically intended to address a growing need for language processing skills, particularly those associated with the rapid growth of artificial intelligence/cognitive technologies.

Systems like Siri or Alexa are built on a pipeline of language processing technologies, ranging from voice and speech recognition to language understanding and automatic question answering. Developing such technologies is a complex process that requires knowledge from both Linguistics and Computer Science.

Computational Linguistics Certificate Courses You Could Take

Computational Linguistics I (3 hrs)
Introduction to the computational analysis of language, focusing on core methods in natural language processing. Combination of theory and implementation (in Python).
NLP in Linguistics (3 hrs)
Advanced course in text processing with Python focusing on the application of traditional machine learning methods and deep learning methods to NLP problems. Understand and apply DEEP LEARNING techniques to classical problems in NLP such as Text classification, Sequence labeling, and Machine Translation.
Computational Linguistics II (3 hrs)
Seminar-style discussion of advanced/specialized topics in computational linguistics. Students design and execute scientific research in the field of CL/NLP.
Introduction to Linguistics for NLP (3 hrs)
An introduction to core principles in linguistics, with a particular focus on developing skills and knowledge relevant for computational linguistics and natural language processing.

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