nlp

问题 I'm using the StanfordCoreNLP API interface to programmatically do some basic NLP. I need to train a model on my own corpus, but I'd like to use the StanfordCoreNLP interface to do it, because it handles a lot of the dry mechanics behind the scenes and I don't need much specialization there. I've trained a CRFClassifier that I'd like to use for NER, serialized to a file. Based on the documentation, I'd think the following would work, but it doesn't seem to find my model and instead barfs on

问题 I am trying to use NLTK for semantic parsing of spoken navigation commands such as "go to San Francisco", "give me directions to 123 Main Street", etc. This could be done with a fairly simple CFG grammar such as S -> COMMAND LOCATION COMMAND -> "go to" | "give me directions to" | ... LOCATION -> CITY | STREET | ... The problem is that this involves non-atomic (more than one word-long) literals such as "go to", which NLTK doesn't seem to be set up for (correct me if I am wrong). The parsing

问题 I have some input text, which contains one or more human person names. I do not have any dictionary for these names. Which Java library can help me to define names from my input text? I looked through OpenNLP, but did not find any example or guide or at least description of how it can be applied into my code. (I saw javadoc, but it is pretty poor documentation for such a project.) I want to find names from some random text. If the input text is "My friend Joe Smith went to the store.", then I

问题 I have a pandas DataFrame consisting of two columns of strings. I would like to create a third column containing the Edit Distance of the two columns. from nltk.metrics import edit_distance df['edit'] = edit_distance(df['column1'], df['column2']) For some reason this seems to go to some sort of infinite loop in the sense that it remains unresponsive for quite some time and then I have to terminate it manually. Any suggestions are welcome. 回答1: The nltk's edit_distance function is for

问题 I'm using CNNs in Keras for an NLP task and instead of max pooling, I'm trying to achieve max over time pooling. Any ideas/hacks on how to achieve this? What I mean by max over time pooling is to pool the highest value, no matter where they are in the vector 回答1: Assuming that your data shape is (batch_size, seq_len, features) you may apply: seq_model = Reshape((seq_len * features, 1))(seq_model) seq_model = GlobalMaxPooling1D()(seq_model) 来源： https://stackoverflow.com/questions/41958115/max

问题 I'm trying to tabulate phones (characters) occurrences in a string, but diacritics are tabulated as characters on their own. Ideally, I have a wordlist in International Phonetic Alphabet, with a fair amount of diacritics and several combinations of them with base characters. I give here a MWE with just one word, but the same goes with list of words and more types of combinations. > word <- "n̥ana" # word constituted by 4 phones: [n̥],[a],[n],[a] > table(strsplit(word, "")) ̥ a n 1 2 2 But the

问题 I have a nested dictionary as such: myDict = {'a': {1:2, 2:163, 3:12, 4:67, 5:84}, 'about': {1:27, 2:45, 3:21, 4:10, 5:15}, 'apple': {1:0, 2: 5, 3:0, 4:10, 5:0}, 'anticipate': {1:1, 2:5, 3:0, 4:8, 5:7}, 'an': {1:3, 2:15, 3:1, 4:312, 5:100}} The outer key is a word, the inner keys are file/document ids the values are the number of times the word (outer key occurs) How do I calculate the sum of the square values to the inner keys? For example for the inner key number 1 , I should get: 2^2 + 27

问题 I have a nested dictionary as such: myDict = {'a': {1:2, 2:163, 3:12, 4:67, 5:84}, 'about': {1:27, 2:45, 3:21, 4:10, 5:15}, 'apple': {1:0, 2: 5, 3:0, 4:10, 5:0}, 'anticipate': {1:1, 2:5, 3:0, 4:8, 5:7}, 'an': {1:3, 2:15, 3:1, 4:312, 5:100}} The outer key is a word, the inner keys are file/document ids the values are the number of times the word (outer key occurs) How do I calculate the sum of the square values to the inner keys? For example for the inner key number 1 , I should get: 2^2 + 27

问题 I'm using Python and nltk + Textblob for some text analysis. It's interesting that you can add a POS for wordnet to make your search for synonyms more specific, but unfortunately the tagging in both nltk and Textblob aren't "compatible" with the kind of input that wordnet expects for it's synset class. Example Wordnet.synsets() requires that the POS you give it is one of n,v,a,r, like so wn.synsets("dog", POS="n,v,a,r") But a standard POS tagging from upenn_treebank looks like JJ, VBD, VBZ,

问题 I'm using Python and nltk + Textblob for some text analysis. It's interesting that you can add a POS for wordnet to make your search for synonyms more specific, but unfortunately the tagging in both nltk and Textblob aren't "compatible" with the kind of input that wordnet expects for it's synset class. Example Wordnet.synsets() requires that the POS you give it is one of n,v,a,r, like so wn.synsets("dog", POS="n,v,a,r") But a standard POS tagging from upenn_treebank looks like JJ, VBD, VBZ,