stanford-nlp

问题 I have a sentence for which i need to identify the Person names alone: For example: sentence = "Larry Page is an American business magnate and computer scientist who is the co-founder of Google, alongside Sergey Brin" I have used the below code to identify the NERs. from nltk import word_tokenize, pos_tag, ne_chunk print(ne_chunk(pos_tag(word_tokenize(sentence)))) The output i received was: (S (PERSON Larry/NNP) (ORGANIZATION Page/NNP) is/VBZ an/DT (GPE American/JJ) business/NN magnate/NN and

问题 How to approach the problem of building a Punctuation Predictor? The working demo for the question can be found in this link. Input Text is as below: "its been a little while Kirk tells me its actually been three weeks now that Ive been using this device right here that is of course the Galaxy S ten I mean Ive just been living with this phone this has been my phone has the SIM card in it I took photos I lived live I sent tweets whatsapp slack email whatever other app this was my smart phone"

问题 I have 2 sentences in my dataset: w1 = I am Pusheen the cat.I am so cute. # no space after period w2 = I am Pusheen the cat. I am so cute. # with space after period When I use NKTL tokenizer (both word and sent), nltk cannot distinct the between cat.I. Here is word tokenize >>> nltk.word_tokenize(w1, 'english') ['I', 'am', 'Pusheen', 'the', 'cat.I', 'am', 'so', 'cute'] >>> nltk.word_tokenize(w2, 'english') ['I', 'am', 'Pusheen', 'the', 'cat', '.', 'I', 'am', 'so', 'cute'] and sent tokenize >>

问题 I have 2 sentences in my dataset: w1 = I am Pusheen the cat.I am so cute. # no space after period w2 = I am Pusheen the cat. I am so cute. # with space after period When I use NKTL tokenizer (both word and sent), nltk cannot distinct the between cat.I. Here is word tokenize >>> nltk.word_tokenize(w1, 'english') ['I', 'am', 'Pusheen', 'the', 'cat.I', 'am', 'so', 'cute'] >>> nltk.word_tokenize(w2, 'english') ['I', 'am', 'Pusheen', 'the', 'cat', '.', 'I', 'am', 'so', 'cute'] and sent tokenize >>

问题 I have 2 sentences in my dataset: w1 = I am Pusheen the cat.I am so cute. # no space after period w2 = I am Pusheen the cat. I am so cute. # with space after period When I use NKTL tokenizer (both word and sent), nltk cannot distinct the between cat.I. Here is word tokenize >>> nltk.word_tokenize(w1, 'english') ['I', 'am', 'Pusheen', 'the', 'cat.I', 'am', 'so', 'cute'] >>> nltk.word_tokenize(w2, 'english') ['I', 'am', 'Pusheen', 'the', 'cat', '.', 'I', 'am', 'so', 'cute'] and sent tokenize >>

问题 I have 2 sentences in my dataset: w1 = I am Pusheen the cat.I am so cute. # no space after period w2 = I am Pusheen the cat. I am so cute. # with space after period When I use NKTL tokenizer (both word and sent), nltk cannot distinct the between cat.I. Here is word tokenize >>> nltk.word_tokenize(w1, 'english') ['I', 'am', 'Pusheen', 'the', 'cat.I', 'am', 'so', 'cute'] >>> nltk.word_tokenize(w2, 'english') ['I', 'am', 'Pusheen', 'the', 'cat', '.', 'I', 'am', 'so', 'cute'] and sent tokenize >>

问题 I have 2 sentences in my dataset: w1 = I am Pusheen the cat.I am so cute. # no space after period w2 = I am Pusheen the cat. I am so cute. # with space after period When I use NKTL tokenizer (both word and sent), nltk cannot distinct the between cat.I. Here is word tokenize >>> nltk.word_tokenize(w1, 'english') ['I', 'am', 'Pusheen', 'the', 'cat.I', 'am', 'so', 'cute'] >>> nltk.word_tokenize(w2, 'english') ['I', 'am', 'Pusheen', 'the', 'cat', '.', 'I', 'am', 'so', 'cute'] and sent tokenize >>

问题 I want to use stanford corenlp for obtaining dependency parser of sentences. In order to using stanford corenlp in python, we need to do the below steps in Google Colab: Install java import os !apt-get install openjdk-8-jdk-headless -qq > /dev/null os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64" Download stanford-corenlp-full-2018-10-05 and extract it. !wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip !unzip stanford-corenlp-full-2018-10-05.zip Change

问题 Very helpfully Stanford NLP core 3.9.2 used to split rolled together Spanish verbs and pronouns This is the 4.0.0 output: The previous version had more .tagger files. These have not been included with the 4.0.0 distribution. Is that the cause. Will be they added back? 回答1: There are some documentation updates that still need to be made for Stanford CoreNLP 4.0.0. A major change is that a new multi-word-token annotator has been added, that makes tokenization conform with the UD standard. So

问题 Very helpfully Stanford NLP core 3.9.2 used to split rolled together Spanish verbs and pronouns This is the 4.0.0 output: The previous version had more .tagger files. These have not been included with the 4.0.0 distribution. Is that the cause. Will be they added back? 回答1: There are some documentation updates that still need to be made for Stanford CoreNLP 4.0.0. A major change is that a new multi-word-token annotator has been added, that makes tokenization conform with the UD standard. So