Post by simranratry20244 on Feb 12, 2024 4:49:51 GMT -5
Although there are several paths to become a computational linguist, the main itinerary usually starts with a degree in Linguistics, Philology, Translation and Interpretation or Humanities , and then completes a more specific master's degree in Natural Language Processing, with which one can also train in statistics. or programming. You can also access this specialization from a degree in Computer Science, Mathematics or similar . In this case, the training is also completed with a master's degree in Natural Language Processing, but reinforcing the general linguistics and Spanish language part (where both the semantic, syntactic and morphological parts would be considered).
There are already some master's degrees that Colombia Telemarketing Data provide for both itineraries from the design, but this is not common in the degrees or master's degrees offered by Spanish universities. Some of the skills or subjects that the most specialized usually understand are: Linguists training Design and development of annotated corpora . One of the most important jobs of a computational linguist is the creation of annotated reference corpora. Specialization courses teach annotation methodologies and processes, as well as for review, and the main annotation tools.
Use of NLP tools and pipelines , such as spaCy or Stanza. These tools allow you to process and enrich a written text with different layers of analysis (tokenization, lemmatization, basic morphology, syntax and semantics). Development of other linguistic resources. Another important part is the creation of other resources such as dictionaries, taxonomies, ontologies, etc. These resources can be used as intermediate processes to generate other more complex resources or to create analysis engines based on lexis and their relationships. Machine learning or deep learning models . Currently, most corpora are intended for training machine learning models. Knowing the different types and knowing how to execute them can also be the task of a computational linguist.
Analysis Metrics . The use and management of the main metrics, both agreement between annotators and model evaluation, is part of the LC's work.
Use of NLP engines or applications, such as sentiment analysis, entity detection, speech technologies, automatic translation, summary and question-answering systems , etc.
Design, configuration and testing of conversational assistants . Depending on the use to which they are intended, chatbots require a training process for the detection of entities and intentions, which is handled by computational linguists. In addition, they have to know how to configure each tool for their design (Dialogflow, Rasa, Watson, etc.), since the operation is quite different. Later, in testing, they will simulate conversations to perfect the chatbot.