Neuro-linguistic Programming (NLP) for Language Teachers: Revalidation of an NLP Scale

NLP (Natural Language Processing) for NLP (Natural Language Programming)

Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will (hopefully) be able to talk to the computer in their own language, rather than learn a specialized language of computer commands. For programming, however, the necessity of a formal programming language for communicating with a computer has always been taken for gr...

Streaming for large scale NLP: Language Modeling

In this paper, we explore a streaming algorithm paradigm to handle large amounts of data for NLP problems. We present an efficient low-memory method for constructing high-order approximate n-gram frequency counts. The method is based on a deterministic streaming algorithm which efficiently computes approximate frequency counts over a stream of data while employing a small memory footprint. We s...

Dynamic Programming for NLP

The strategy of dynamic programming reduces the complexity of a search problem which decomposes into frequently-reused instances of the same problem. You encountered dynamic programming for n-gram segmentation in HW4. We have also discussed two more dynamic programming algorithms in lecture: Viterbi and forward-backward. For HW5, you will need to implement the Viterbi algorithm. The forward-bac...

Neuro-Linguistic Programming (NLP): A Case of Unorthodox Knowledge in HRD?

Linguistically Naïve != Language Independent: Why NLP Needs Linguistic Typology

In this position paper, I argue that in order to create truly language-independent NLP systems, we need to incorporate linguistic knowledge. The linguistic knowledge in question is not intricate rule systems, but generalizations from linguistic typology about the range of variation in linguistic structures across languages.