Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …

Recently, the automatic prediction of personality traits has received a lot of attention.
Specifically, personality trait prediction from multimodal data has emerged as a hot topic …

Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

Computational linguistics has enabled the introduction of objective tools that measure some
of the symptoms of schizophrenia, including the coherence of speech associated with formal …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Continuous word representations, trained on large unlabeled corpora are useful for many
natural language processing tasks. Popular models that learn such representations ignore …

We propose two neural network architectures for nested named entity recognition (NER), a
setting in which named entities may overlap and also be labeled with more than one label …

We propose a hierarchical attention network for document classification. Our model has two
distinctive characteristics:(i) it has a hierarchical structure that mirrors the hierarchical …