Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

Deep learning has emerged as a powerful machine learning technique that learns multiple
layers of representations or features of the data and produces state‐of‐the‐art prediction …

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

Humans convey their intentions through the usage of both verbal and nonverbal behaviors
during face-to-face communication. Speaker intentions often vary dynamically depending on …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

One key issue in text mining and natural language processing is how to effectively represent
documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a …

We propose learning sentiment-specific word embeddings dubbed sentiment embeddings
in this paper. Existing word embedding learning algorithms typically only use the contexts of …

Sentiment analysis is the computational study of people's opinions, sentiments, emotions,
moods, and attitudes. This fascinating problem offers numerous research challenges, but …

Text preprocessing is often the first step in the pipeline of a Natural Language Processing
(NLP) system, with potential impact in its final performance. Despite its importance, text …