The proliferation of fake news on social media has opened up new directions of research for
timely identification and containment of fake news and mitigation of its widespread impact on …

Probabilistic finite-state string transducers (FSTs) are extremely popular in natural language
processing, due to powerful generic methods for applying, composing, and learning them …

Abstract Language modeling on large-scale datasets improves performance of various
downstream tasks. The validation pre-training loss is often used as the evaluation metric for …

Graph-based text representation is one of the important preprocessing steps in data and text
mining, Natural Language Processing (NLP), and information retrieval approaches. The …

We demonstrate that an unlexicalized PCFG can parse much more accurately than
previously shown, by making use of simple, linguistically motivated state splits, which break …

This paper investigates the role of resource allocation as a source of processing difficulty in
human sentence comprehension. The paper proposes a simple information-theoretic …

Generic unstructured neural networks have been shown to struggle on out-of-distribution
compositional generalization. Compositional data augmentation via example recombination …

Learning general functional dependencies between arbitrary input and output spaces is one
of the key challenges in computational intelligence. While recent progress in machine …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

We study a formalization of the grammar induction problem that models sentences as being
generated by a compound probabilistic context-free grammar. In contrast to traditional …