Distributed vector representations or embeddings map variable length text to dense fixed
length vectors as well as capture prior knowledge which can transferred to downstream …

Word embeddings provide quantitative representations of word semantics and the
associations between word meanings in text data, including in large repositories in media …

Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …

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 …

Despite the fast developmental pace of new sentence embedding methods, it is still
challenging to find comprehensive evaluations of these different techniques. In the past …

With the rapid growth of medical costs, the control of medical expenses has been becoming
an important task of Health Insurance Department. Traditional medical insurance settlement …

In the last few years, the natural language processing community has witnessed advances
in neural representations of free texts with transformer-based language models (LMs). Given …

While one of the first steps in many NLP systems is selecting what pre-trained word
embeddings to use, we argue that such a step is better left for neural networks to figure out …

Media has a substantial impact on public perception of events, and, accordingly, the way
media presents events can potentially alter the beliefs and views of the public. One of the …

In the history of natural language processing (NLP) development, the representation of
words has always been a significant research topic. In this survey, we provide a …