Computational linguistics: what is it and its relationship with Big Data

Computational linguistics was born as a specific area within the field of Artificial Intelligence. The pioneers in this area were computing experts in Natural Language Processing through the use of computers.

After the creation of different associations related to it, computational linguistics consolidated during the 70s and 80s decades. Today, the term computational linguistics was considered by many as a synonym of Natural Language Processing (NLP).

Computational linguistics presents both a theoretical framework as well as practical applications. The theory consists of a mixture of theoretical lingüistics and cognitive science, while its applications focus on modeling the use of human language in both computers and software platforms.

Specialists in this discipline typically present knowledge in both areas and, additionally, in order to be able to work with the huge volumes of required information, basic notions of Data Science, Big Data, Machine Learning and Deep Learning are also required.

 

What is computational linguistics?

Computational linguistics refers to the application of computer sciences to the analysis, synthesis and understanding of both written and spoken language. This discipline presents a wide variety of uses, from automatic translation to voice recognition, text synthesizers or search engines.

Computational linguistics offers a global vision of human thinking and intelligence applied to the computing field. Computers trained in linguistics facilitate human interactions with them and with software and make textual and audio resources (among others) available in different languages.

Some of the goals of computational linguistics include:

• Text translation
• Text retrieving according to specific themes
• Textual analysis or spoken-text analysis in the search of a context, sentiment or other related qualities
• Provide answers to different questions, including those that require an inference or more descriptive or discursive answers
• Creation of text summaries
• Chatbots, both for experiments and for providing assistance in certain websites
• Creation of complex programs which are able to undertake more complex tasks such as shopping, travel planning or planning maintenance operations for installations.

Work in computational linguistics presents the aim of improving the relationship between computers and basic language

Computational linguistics imply the creation of programs that can be used to process and produce language, both written or oral. As such, great volumes of data are needed. It’s here where the data scientists and Big Data analysis take place.

 

Theoretical computational linguistics

This field in natural language processing includes everything related to the development of formal grammar and semantic theories based on formal logics or symbolic approaches.

The areas of theoretical study in this field include:

• Computational complexity. In this case, work involves looking at theory related to automatons and the applications for grammar and sentiment analysis.
• Computational semantics. It involves the definition of suitable logic to represent the linguistic meaning.

 

Applied computational linguistics

In this case, we refer to the application of theory using methods such as automatic learning. However, traditionally in the field of computational linguistics, statistical methods have been used.

Currently, the use of natural language processing in machines, software and computers combines a series of techniques which go from Big Data to artificial neural networks.

 

How is the field of computational linguistics related to Big Data?

As we’ve mentioned above, in order for the work of computational linguistics to be possible, big quantities of information are needed. With the analysis of this data, it’s possible to obtain guidelines, establishing patterns about how human language works to later transfer it to software and computers.

As such, the construction of applications using computational linguistics implies data scientists must analyze huge volumes of written and spoken language in the shape of structured and unstructured data. This is facilitated through Big Data techniques or Data Science.

Specialists in this field are well-trained and specialized professionals in several areas

Traditionally, models used by computational linguistics benefit from data collecting, but require annotations and human markings to improve their later applications. This used to be done using a corpus of brute text, analyzed and processed by linguists.

However, as time went by, with the evolution of the discipline and the increase of available information, experts have started working from a Data Science and Big Data approach in order to manage data and create the corpus of pure text that linguists work on.

 

Get training in Big Data!

Computational linguistics and Big Data are closely linked. The huge volumes of information that must be managed, both as structured and unstructured data, require Data Science and Big Data specialists. This means they’re experts in knowing how to treat data, so that specialists in natural language processing can later work with it.

In such a context, getting training in Big Data is a good idea if you’re looking for a professional field which is constantly expanding. Both for computational linguistics and other applications, getting training in data analysis is getting involved in a discipline with a bright future ahead.

At Tokyo School we’re specialists in offering training in new technologies for professionals, and Big Data is no exception. We offer a Big Data course that you can use to take the plunge into this exciting field.

Would you like to learn more about this course or our school? Do not hesitate to ask us! Get in touch with us and you’ll get all the information you need to make your decision. We can only encourage you to work to improve your future and Big Data is a great option for that.