Entity extraction, which is also named as named-entity recognition (NERI),entity checking and entity identification which is a subtask of information extraction with a goal of detecting and classifying a text in predefined categories.
A Named Entity Extraction feature automatically pulls a proper nouns from a text and which determines their sentiment from a document.Salience Engine and Semantria Excel/API all come with a lists of pre-installed machine learning models.
Build up a lists of custom entities for tracking for a discovery purposes, to find entites specfic.
Named entity extraction gives user a insight about what people are saying about a product.
POS tagging a plus generic chunk parsing alone does not solve he NE problem.
Postmodification.
Wrong structure when generic chunk parsing.
Coordination od unit measures.
Generic chunk analysis.
Productivity of name creation requires lexicon free pattern recognition.
NE ambiguity requires resolution methods.
Fine-grained NE classfication needs fined grained decision making methods.
Multi-linguality.
System-based adaption.
1.Fast development cycle.
2.manual spefication too expensive.
3.Language-independence of learning algorithms.
Current approaches show near-human as performance.
High innovation potential.
1.Supervised learning.
2.Unsupervised learning.
3.Main features used by CoNLL 2003 systems.
1.Unsupervised NE idea.
Define manually for a small set of trusted seeds.
Training then only uses un-labled data only.
Extract and generalize a pattens from a context of a seeds.
2.Two Categories of Rules.
1.Paradigmatic or spelling.
2.Syntagmatic or contextual.
3.GNs and PNs.
Not necessary capitalized
Name boundaries are non-trivial to identify.
Ambiguity.
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