nlp

问题 I do keyword in context analysis with quanteda for ngrams and tokens and it works well. I now want to do it for skipgrams, capture the context of "barriers to entry" but also "barriers to [...] [and] entry. The following code a kwic object which is empty but I don't know what I did wrong. dcc.corpus refers to the text document. I also used the tokenized version but nothing changes. The result is: "kwic object with 0 rows" x <- tokens("barriers entry") ntoken_test <- tokens_ngrams(x, n = 2,

问题 I do keyword in context analysis with quanteda for ngrams and tokens and it works well. I now want to do it for skipgrams, capture the context of "barriers to entry" but also "barriers to [...] [and] entry. The following code a kwic object which is empty but I don't know what I did wrong. dcc.corpus refers to the text document. I also used the tokenized version but nothing changes. The result is: "kwic object with 0 rows" x <- tokens("barriers entry") ntoken_test <- tokens_ngrams(x, n = 2,

问题 I am running a scrape and process routine in Python3 - but some of the sentences I get are garbage. I would like to reject these but cant figure out how to do it. I am using POS tagging and chunking with NLTK but that doesn't seem to help me identify non-valid sentences. The number of NNs, VBs etc. doesn't seem to be any different in a garbage "sentence" than a good one. I guess I am just looking for a simple method to score the grammar of a sentence and reject ones with too many "errors". I

问题 I instantiated a sklearn.feature_extraction.text.CountVectorizer object by passing a vocabulary through the vocabulary argument, but I get a sklearn.utils.validation.NotFittedError: CountVectorizer - Vocabulary wasn't fitted. error message. Why? Example: import sklearn.feature_extraction import numpy as np import pickle # Save the vocabulary ngram_size = 1 dictionary_filepath = 'my_unigram_dictionary' vectorizer = sklearn.feature_extraction.text.CountVectorizer(ngram_range=(ngram_size,ngram

问题 Since I was told Spacy was such a powerful Python module for natural speech processing, I am now desperately looking for a way to group words together to more than noun phrases, most importantly, prepositional phrases. I doubt there is a Spacy function for this but that would be the easiest way I guess (SpacySpaCy import is already implemented in my project). Nevertheless, I'm open for any possibility of phrase recognition/ chunking. 回答1: Here's a solution to get PPs. In general you can get

问题 I am trying to build a document retrieval model that returns most documents ordered by their relevancy with respect to a query or a search string. For this I trained a doc2vec model using the Doc2Vec model in gensim. My dataset is in the form of a pandas dataset which has each document stored as a string on each line. This is the code I have so far import gensim, re import pandas as pd # TOKENIZER def tokenizer(input_string): return re.findall(r"[\w']+", input_string) # IMPORT DATA data = pd

来源： https://stackoverflow.com/questions/64543626/how-to-work-with-n-grams-for-classification-tasks

来源： https://stackoverflow.com/questions/51463453/running-glove-on-windows

来源： https://stackoverflow.com/questions/64704015/nlp-classification-labels-have-many-similarirites-replace-to-only-have-one

来源： https://stackoverflow.com/questions/63708214/how-to-make-a-spacy-matcher-pattern-using-verb-tense-mood