Named entity recognition using a twisted neural network
In this paper, http://www.cs.cmu.edu/~leili/pubs/lu-baylearn2015-twinet.pdf, the authors look at the problem of recognizing entities in natural language. This is often the first step in question answering, conversations, and a host of other NLP use cases. For a sequence of text tokens, a named entity recognizer identifies chunks of tokens that belong to a predefined category of persons and organizations.
Example of a named entity recognition
IOB tagging system is one of the convention for NER.
The IOB Tagging system contains tags of the form:
B-{CHUNK_TYPE}: for the word in the Beginning chunkI-{CHUNK_TYPE}: for words Inside the chunkO: Outside any chunkB-PERSON: Person EntityB-GPE: Geopolitical Entity
Th following text shows example of a names entities in a sentence:
John has lived in Britain for 14 years . B-PERSON O O O B-GPE O B-CARDINAL I-CARDINAL O
However, it is quite challenging due to two reasons:
- Entity...
