Explainable natural language processing with matrix product states

Natural Language Processing with Python

Stochastic matrix product states.

The concept of stochastic matrix product states is introduced and a natural form for the states is derived. This allows us to define the analogue of Schmidt coefficients for steady states of nonequilibrium stochastic processes. We discuss a new measure for correlations which is analogous to entanglement entropy, the entropy cost S(C), and show that this measure quantifies the bond dimension nee...

Entanglement classification with matrix product states

We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between ent...

Matrix and Tensor Factorization Methods for Natural Language Processing

Tensor and matrix factorization methods have attracted a lot of attention recently thanks to their successful applications to information extraction, knowledge base population, lexical semantics and dependency parsing. In the first part, we will first cover the basics of matrix and tensor factorization theory and optimization, and then proceed to more advanced topics involving convex surrogates...

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and informa...