nlp

问题 I have 45000 text records in my dataframe. I wanted to convert those 45000 records into word vectors so that I can train a classifier on the word vector. I am not tokenizing the sentences. I just split the each entry into list of words. After training word2vec model with 300 features, the shape of the model resulted in only 26000. How can I preserve all of my 45000 records ? In the classifier model, I need all of those 45000 records, so that it can match 45000 output labels. 回答1: If you are

问题 I am trying to work with a very large corpus of about 85,000 tweets that I'm trying to compare to dialog from television commercials. However, due to the size of my corpus, I am unable to process the cosine similarity measure without getting the "Error: cannot allocate vector of size n" message, (26 GB in my case). I am already running R 64 bit on a server with lots of memory. I've also tried using the AWS on the server with the most memory, (244 GB), but to no avail, (same error). Is there a

问题 I'm trying to create a very simple RESTful Web Service with Spring Boot to perform NLP to the content passed as a parameter. You can find it on my GitHub. For some reason, I can't deploy it to my Tomcat container in my home server as a WAR (see here), therefore I decided at least to try to set it up as a runnable JAR. If I run it on my development machine by invoking: java -jar -Xss32M -Xmx8G -XX:+UseG1GC -XX:+UseStringDeduplication ClearWS-0.1.0.jar it works like a charm. If I point my

问题 Is there a list of languages supported by Botpress Native NLP? Is Czech languages supported by default? Or only by using third party as Watson? 回答1: I assume you were using early Botpress 11. In those versions, botpress was using fasttext to train word vectors on the data you were providing with a basic whitespace tokenizer. Couple of pre-trained languages were offered but not Czech sadly. In the newer version of Botpress (12.x), NLU structure seems to have drastically changed and more

问题 I want to make a chatbot that will answer the questions based on the given document. E.g, if I have hundreds of documents and I want to get some information from it but don't know which information is on which line of the page so I have to spend some time and effort to search. I want a chatbot that will learn from those documents and give answers form that documents. Is there any available service that can full fill my needs? What if I want to make a model by myself what tools/libraries do I

问题 I have a dataset containing "facts" recognizable to prolog, i.e.: 'be'('mr jiang', 'representative of china'). 'support'('the establishment of the sar', 'mr jiang'). 'be more than'('# distinguished guests', 'the principal representatives'). 'end with'('the playing of the british national anthem', 'hong kong'). 'follow at'('the stroke of midnight', 'this'). 'take part in'('the ceremony', 'both countries'). 'start at about'('# pm', 'the ceremony'). 'end about'('# am', 'the ceremony'). I want

问题 I am playing around with NLTK and the module freqDist import nltk from nltk.corpus import gutenberg print(gutenberg.fileids()) from nltk import FreqDist fd = FreqDist() for word in gutenberg.words('austen-persuasion.txt'): fd[word] += 1 newfd = sorted(fd, key=fd.get, reverse=True)[:10] So I am playing around with NLTK and have a question regarding the sort portion. When I run the code like this it properly sorts the freqDist object. However when I run it with get() instead of get I encounter

问题 Greetings NLP Experts, I am using the Stanford CoreNLP software package to produce constituency parses, using the most recent version (3.9.2) of the English language models JAR, downloaded from the CoreNLP Download page. I access the parser via the Python interface from the NLTK module nltk.parse.corenlp. Here is a snippet from the top of my main module: import nltk from nltk.tree import ParentedTree from nltk.parse.corenlp import CoreNLPParser parser = CoreNLPParser(url='http://localhost