Emerging opportunities and challenges for the future of reservoir computing



natural language processing in soft computing :: Article Creator

CSCA 5832: Fundamentals Of Natural Language Processing

Duration: 7 hours, 18 minutes

This final week explores how words can be represented as vectors in a high-dimensional space, allowing computational models to capture semantic relationships between words. You will learn about both sparse and dense vector representations, including TF-IDF, Pointwise Mutual Information (PMI), Latent Semantic Analysis (LSA), and Word2Vec. The module also covers techniques for evaluating and applying word embeddings.


SFU Natural Language Laboratory

Natural Language Processing (NLP) is the automatic analysis of human languages such as English, Korean, etc. By computer algorithms. Unlike programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations.

Imagine that you talk in English on the phone and at the other end of the line your words are spoken in Chinese. Imagine a computer animated representation of yourself speaking fluently what you have written in an email. These are only some of the uses of NLP.

The Natural Language Lab at SFU is one of the largest North American labs working on natural language processing and computational linguistics. We have a strong relationship with the natural language industry in Canada, the National Research Council, and various research groups around the world and within SFU. In 1999, lab researchers formed a company, Axonwave Software Inc., which uses language technology software.

Our lab is active in areas of Information extraction, Machine translation, Summarization, and Statistical parsing. We have worked with: Arabic, Chinese, Czech, English, French, Hindi, Korean and Spanish. 





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