Large-scale photonic natural language processing

Large - Scale Semi - Supervised Learning for Natural Language Processing

Natural Language Processing (NLP) develops computational approaches to processing language data. Supervised machine learning has become the dominant methodology of modern NLP. The performance of a supervised NLP system crucially depends on the amount of data available for training. In the standard supervised framework, if a sequence of words was not encountered in the training set, the system c...

Using natural language processing to construct large - scale hypertext systems

theory of how texts are connected (a theory of text association) and partial theories of what the text is describing (domain theories). However, systems built in this way, such as ASK Systems [Ferguson, et al . 1992], are difficult to build even when the domain theories and a theory of text association are in place . Natural language understanding technologies that take advantage of underlying ...

Large-Scale Paraphrasing for Natural Language Understanding

We examine the application of data-driven paraphrasing to natural language understanding. We leverage bilingual parallel corpora to extract a large collection of syntactic paraphrase pairs, and introduce an adaptation scheme that allows us to tackle a variety of text transformation tasks via paraphrasing. We evaluate our system on the sentence compression task. Further, we use distributional si...

Large-scale network involvement in language processing.

Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message...