named-entity-extraction

import nltk from itertools import groupby def get_continuous_chunks(tagged_sent): continuous_chunk = [] current_chunk = [] for token, tag in tagged_sent: if tag != "O": current_chunk.append((token, tag)) else: if current_chunk: # if the current chunk is not empty continuous_chunk.append(current_chunk) current_chunk = [] # Flush the final current_chunk into the continuous_chunk, if any. if current_chunk: continuous_chunk.append(current_chunk) return continuous_chunk ne_tagged_sent = [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'), (

I'm currently on a learning project to extract an individuals name from their CV/Resume. Currently I'm working with Stanford-NER and OpenNLP which both perform with a degree of success out of the box on, tending to struggle on "non-western" type names (no offence intended towards anybody). My question is - given the general lack of sentence structure or context in relation to an individuals name in a CV/Resume, am I likely to gain any significant improvement in name identification by creating something akin to a CV corpus? My initial thoughts are that I'd probably have a more success by

I want to be able to match efficiently thousands of regexps out of GBs of text knowing that most of these regexps will be fairly simple, like: \bBarack\s(Hussein\s)?Obama\b \b(John|J\.)\sBoehner\b etc. My current idea is to try to extract out of each regexp some kind of longest substring, then use Aho-Corasick to match these substrings and eliminate most of the regexp and then match all the remaining regexp combined. Can anyone think of something better? You can use (f)lex to generate a DFA, which recognises all the literals in parallel . This might get tricky if there are too many wildcards