tokenize

来源： https://stackoverflow.com/questions/63982177/how-to-tokenize-new-vocab-in-spacy

问题 I use Stanford NLP for string tokenization in my classification tool. I want to get only meaningful words, but I get non-word tokens (like --- , > , . etc.) and not important words like am , is , to (stop words). Does anybody know a way to solve this problem? 回答1: This is a very domain-specific task that we don't perform for you in CoreNLP. You should be able to make this work with a regular expression filter and a stopword filter on top of the CoreNLP tokenizer. Here's an example list of

问题 How to Split the given String for the given Delimiter. Ex: INPUT String => '1,2,3,4,5' Delimiter => ',' OUTPUT 1 2 3 4 5 回答1: What about this? The regular expression allows for null list elements too. SQL> with tbl(str) as ( 2 select '1,2,,4,5' from dual 3 ) 4 select regexp_substr(str, '(.*?)(,|$)', 1, level, null, 1) element 5 from tbl 6 connect by level <= regexp_count(str, ',')+1; ELEMENT -------- 1 2 4 5 SQL> See this post for a function that returns a list element: REGEX to select nth

问题 How to Split the given String for the given Delimiter. Ex: INPUT String => '1,2,3,4,5' Delimiter => ',' OUTPUT 1 2 3 4 5 回答1: What about this? The regular expression allows for null list elements too. SQL> with tbl(str) as ( 2 select '1,2,,4,5' from dual 3 ) 4 select regexp_substr(str, '(.*?)(,|$)', 1, level, null, 1) element 5 from tbl 6 connect by level <= regexp_count(str, ',')+1; ELEMENT -------- 1 2 4 5 SQL> See this post for a function that returns a list element: REGEX to select nth

问题 I have String some like this (Customer.Activity == "Car Loan") i am using below code to split the String using StringTokenizer in java import java.util.ArrayList; import java.util.StringTokenizer; public class StringTokenizerClass { public ArrayList<String> stringTokenizer(String str) { StringTokenizer Tokenizer = new StringTokenizer(str); ArrayList<String> tokenList = new ArrayList<String>(); while (Tokenizer.hasMoreTokens()) { tokenList.add(Tokenizer.nextToken()); } return (tokenList); } }

问题 I've been looking to use Hugging Face's Pipelines for NER (named entity recognition). However, it is returning the entity labels in inside-outside-beginning (IOB) format but without the IOB labels. So I'm not able to map the output of the pipeline back to my original text. Moreover, the outputs are masked in BERT tokenization format (the default model is BERT-large). For example: from transformers import pipeline nlp_bert_lg = pipeline('ner') print(nlp_bert_lg('Hugging Face is a French

问题 I've been looking to use Hugging Face's Pipelines for NER (named entity recognition). However, it is returning the entity labels in inside-outside-beginning (IOB) format but without the IOB labels. So I'm not able to map the output of the pipeline back to my original text. Moreover, the outputs are masked in BERT tokenization format (the default model is BERT-large). For example: from transformers import pipeline nlp_bert_lg = pipeline('ner') print(nlp_bert_lg('Hugging Face is a French

问题 I am processing large amount of text for custom (NER) Named Entity Recognition using Spacy. For text pre-processing I am using nltk for tokenization..etc. I am able to process one of my custom entities which is based on simple strings. But the other custom entity is a combination of number and certain text (20 BBLs for example). The word_tokenize method from nltk.tokenize tokenizes 20 and 'BBLs' separately each as a separate token. What I want is to treat them (the number and the 'BBLs'

问题 I am processing large amount of text for custom (NER) Named Entity Recognition using Spacy. For text pre-processing I am using nltk for tokenization..etc. I am able to process one of my custom entities which is based on simple strings. But the other custom entity is a combination of number and certain text (20 BBLs for example). The word_tokenize method from nltk.tokenize tokenizes 20 and 'BBLs' separately each as a separate token. What I want is to treat them (the number and the 'BBLs'

问题 I'm trying to train a random forest regressor to predict the hourly wage of an employee given the job description supplied. Note, I've signed an NDA and cannot upload real data. The below "observation" is synthetic: sample_row = {'job_posting_id': 'id_01', 'buyer_vertical': 'Business Services', 'currency': 'USD', 'fg_onet_code': '43-9011.00', 'jp_title': 'Computer Operator', 'jp_description': "Performs information security-related risk and compliance activities, including but not limited to