Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

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 …

Traditional supervised learning algorithms assume that the training data and the test data
are drawn from the same distribution. Models that are purely trained from training data are …

Named entity recognition (NER) is the problem of locating and categorizing important nouns
and proper nouns in a text. In this chapter, we review the general state of research on entity …

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 …

Extracting sentiment and topic lexicons is important for opinion mining. Previous works have
showed that supervised learning methods are superior for this task. However, the …

Named-entity recognition (NER) is an important task required in a wide variety of
applications. While rule-based systems are appealing due to their well-known …