named-entity-extraction

How to perform entity linking to local knowledge graph?

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问题 I'm building my own knowledge base from scratch, using articles online. I am trying to map the entities from my scraped SPO triples (the Subject and potentially the Object) to my own record of entities which consist of listed companies which I scraped from some other website. I've researched most of the libraries, and the method are focused on mapping entities to big knowledge bases like Wikipedia, YAGO, etc., but I'm not really sure how to apply those techniques to my own knowledge base.

Efficient Named Entity Recognition in R

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问题 I have below code in R for extracting person and locations from text: library(rvest) library(NLP) library(openNLP) page = pdf_text("C:/Users/u214738/Documents/NER_Data.pdf") text = as.String(page) sent_annot = Maxent_Sent_Token_Annotator() word_annot = Maxent_Word_Token_Annotator() install.packages("openNLPmodels", repos = "http://datacube.wu.ac.at/src/contrib/", type = "source") install.packages("openNLPmodels.en", repos = "http://datacube.wu.ac.at/", type = "source") install.packages(

Training n-gram NER with Stanford NLP

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问题 Recently I have been trying to train n-gram entities with Stanford Core NLP. I have followed the following tutorials - http://nlp.stanford.edu/software/crf-faq.shtml#b With this, I am able to specify only unigram tokens and the class it belongs to. Can any one guide me through so that I can extend it to n-grams. I am trying to extract known entities like movie names from chat data set. Please guide me through in case I have mis-interpretted the Stanford Tutorials and the same can be used for

Fast algorithm to extract thousands of simple patterns out of large amounts of text

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问题 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? 回答1: You can use (f)lex to generate a

How do you find the list of wikidata (or freebase or DBpedia) topics that a text is about?

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问题 I am looking for a solution to extract the list of concepts that a text (or html) document is about. I'd like the concepts to be wikidata topics (or freebase or DBpedia). For example " Bad is a song by Mikael Jackson " should return Michael Jackson (the artist, wikidata Q2831) and Bad (the song, wikidata Q275422). As this example shows, the system should be robust to spelling mistakes (Mikael) and ambiguity (Bad). Ideally the system should work across multiple languages, it should work both

Named entity recognition with a small data set (corpus)

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问题 I want to develop a Named entity recognition system in Persian language but we have a small NER tagged corpus for training ans test. Maybe In the future we'll have a better and bigger corpus. By the way I need a solution that get incrementally the better performance whenever the new data added without with merge the new data with old data and training from scratch. Is there any solution ? 回答1: Yes. With your help: it is a work in progress. It is JS and "No training ..." Please see https:/

How do I do Entity Extraction in Lucene

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问题 I m trying to do Entity Extraction (more like matching) in Lucene. Here is a sample workflow: Given some text (from a URL) AND a list people names, try to extract names of people from the text. Note: Names of people are not completely normalized. e.g. Some are Mr. X, Mrs. Y and some are just John Doe, X and Y. Other prefixes and suffixes to think about are Jr., Sr., Dr., I, II ... etc. (dont let me get started with non US names). I am using Lucene MemoryIndex to create an in memory index of

Named entity recognition with a small data set (corpus)

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I want to develop a Named entity recognition system in Persian language but we have a small NER tagged corpus for training ans test. Maybe In the future we'll have a better and bigger corpus. By the way I need a solution that get incrementally the better performance whenever the new data added without with merge the new data with old data and training from scratch. Is there any solution ? Yes. With your help: it is a work in progress. It is JS and "No training ..." Please see https://github.com/redaktor/nlp_compromise/ ! It is a fork where I worked on NER during the last days and it will be

How to store ner result in json/ database

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问题 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',

Training n-gram NER with Stanford NLP

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Recently I have been trying to train n-gram entities with Stanford Core NLP. I have followed the following tutorials - http://nlp.stanford.edu/software/crf-faq.shtml#b With this, I am able to specify only unigram tokens and the class it belongs to. Can any one guide me through so that I can extend it to n-grams. I am trying to extract known entities like movie names from chat data set. Please guide me through in case I have mis-interpretted the Stanford Tutorials and the same can be used for the n-gram training. What I am stuck with is the following property #structure of your training file;