Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Textual information is becoming available in abundance on the web, arising the requirement
of techniques and tools to extract the meaningful information. One of such an important …

We consider the task of named entity recognition for Chinese social media. The long line of
work in Chinese NER has focused on formal domains, and NER for social media has been …

Applying natural language processing for mining and intelligent information access to tweets
(a form of microblog) is a challenging, emerging research area. Unlike carefully authored …

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

The rise of social media has brought computational linguistics in ever-closer contact with
bad language: text that defies our expectations about vocabulary, spelling, and syntax. This …

In recent years, online social networking has revolutionized interpersonal communication.
The newer research on language analysis in social media has been increasingly focusing …

Named entity recognition, and other information extraction tasks, frequently use linguistic
features such as part of speech tags or chunkings. For languages where word boundaries …

Twitter is the largest source of microblog text, responsible for gigabytes of human discourse
every day. Processing microblog text is difficult: the genre is noisy, documents have little …

Abstract Information on the Internet is fragmented and presented in different data sources,
which makes automatic knowledge harvesting and understanding formidable for machines …