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In natural language processing, named-entity recognition is the challenge of, well, recognizing named entities such as organizations, places, and most importantly names.
There is a major challenge in this though that I call that of synonymy: The Count and Dracula are in fact referring to the same person, but it it possible that this is never discussed directly in the text.
What would be the best algorithm to resolve these synonyms?
If there is a feature for this in any Python-based library, I'm eager to be educated. I'm using NLTK.
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In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively.
When we read a text, we naturally recognize named entities like people, values, locations, and so on. For example, in the sentence “Mark Zuckerberg is one of the founders of Facebook, a company from the United States” we can identify three types of entities: “Person”: Mark Zuckerberg. “Company”: Facebook.
Named Entity Recognition is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string.
There are three major approaches to NER: lexicon-based, rule-based, and machine learning based.
You are describing a problem of coreference resolution and named entity linking. I'm providing separate links as I am not entirely sure which one you meant.
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