Overview of the Course

Over the past decade, there has been a remarkable cross-fertilization between the fields of psycholinguistics and natural language processing with insights from each field enhancing the development of theory, methodology, and resources in the other.

Developments in statistical NLP have lead to a much larger availability of textual resources for psycholinguistic research and have made it possible for psycholinguists to quickly develop specific resources such as corpora and word-frequency lists. As a result, there has been an explosive growth in behavioral and linguistic data that can be leveraged for psycholinguistic research.

Our increasingly networked and technological society has also spurred development of techniques such as natural language understanding and machine translation. This has led to a re-birth of artificial intelligence known as deep learning, which can be traced back to learning theory developed in psychology in (Rescorla & Wagner, 1977) and can again be applied to research on human language processing (e.g., Mandera, Keuleers, & Brysbaert, 2017).

These developments are quickly changing psycholinguistic research. From a field that for decades has been dominated by small-scale one-time controlled laboratory experiments it is becoming a dynamic research enterprise relying on reusable and distributed data generation processes leveraging crowd participation (Keuleers & Balota, 2015).

Knowledge of these developments and techniques is becoming indispensable for researchers working in any area of cognitive science that deals directly or indirectly with language or linguistic resources.

Objectives of the Course

The primary objectives for this course are:

1) To explore the mutual connections between the fields of natural language processing and language psychology, from a historical perspective.

2) To teach participants natural language processing techniques that can be applied to behavioral research.

3) To instill in participants the mindset to do creative psycholinguistic research that takes full advantage of NLP techniques and big data.

Eligibility

The course can be attended by students from diverse fields as computer science, linguistics, cognitive psychology, cognitive sciences, psycholinguistics, Natural Language Processing, Data Mining, Big Data and so on.

Students at all levels BTech/MTech, BSc./MSc. and from early level PhDs across these disciplines and also interested young faculty from reputed universities and technical institutions are welcome to attend the course.

*However, preference will be given to early career PhD Students, young Faculty and exceptional Masters’s Students.