A sentiment analysis has received a lot of attention from researchers working in the fields of
natural language processing and text mining. However, there is a lack of annotated data …

Named Entity Recognition (NER) is essential for some Natural Language Processing (NLP)
tasks. Previous researchers gave a survey of NER in statistical machine learning era …

Semantic similarity detection is a fundamental task in natural language understanding.
Adding topic information has been useful for previous feature-engineered semantic similarity …

Named Entity Recognition (NER) performance often degrades rapidly when applied to target
domains that differ from the texts observed during training. When in-domain labelled data is …

Abstract The challenges of Named Entities Recognition (NER) for tweets lie in the
insufficient information in a tweet and the unavailability of training data. We propose to …

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP)
task to extract important concepts (named entities) from clinical narratives. Researchers …

Automatic classification of sentiment is important for numerous applications such as opinion
mining, opinion summarization, contextual advertising, and market analysis. Typically …

Digitizing large collections of scientific literature can enable new informatics approaches for
scientific analysis and meta-analysis. However, most content in the scientific literature is …

A large amount of detailed data about bridge conditions and maintenance actions are buried
in bridge inspection reports without being used. Information extraction and data analytics …

Abstract Named Entity Recognition (NER) is a key NLP task, which is all the more
challenging on Web and user-generated content with their diverse and continuously …