The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their
sentiments are analysed and sentiments are evolved over time, is getting much attention …

Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Dependency trees convey rich structural information that is proven useful for extracting
relations among entities in text. However, how to effectively make use of relevant information …

Dependency trees help relation extraction models capture long-range relations between
words. However, existing dependency-based models either neglect crucial information (eg …

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 …

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Log-linear models and more recently neural network models used for supervised relation
extraction requires substantial amounts of training data and time, limiting the portability to …

Relation classification is a crucial ingredient in numerous information extraction systems
seeking to mine structured facts from text. We propose a novel convolutional neural network …

Existing relation classification methods that rely on distant supervision assume that a bag of
sentences mentioning an entity pair are all describing a relation for the entity pair. Such …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …