nlp

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . Does anyone know of a .NET date/time parser similar to Chronic for Ruby (handles stuff like "tomorrow" or "3pm next thursday")? Note: I do write Ruby (which is how I know about Chronic) but this project must use .NET. 回答1: We developed exactly what you are looking for on an internal project. We are thinking of

问题 How do I detect what language a text is written in using NLTK? The examples I've seen use nltk.detect , but when I've installed it on my mac, I cannot find this package. 回答1: Have you come across the following code snippet? english_vocab = set(w.lower() for w in nltk.corpus.words.words()) text_vocab = set(w.lower() for w in text if w.lower().isalpha()) unusual = text_vocab.difference(english_vocab) from http://groups.google.com/group/nltk-users/browse_thread/thread/a5f52af2cbc4cfeb?pli=1&safe

问题 Going through the NLTK book, it's not clear how to generate a dependency tree from a given sentence. The relevant section of the book: sub-chapter on dependency grammar gives an example figure but it doesn't show how to parse a sentence to come up with those relationships - or maybe I'm missing something fundamental in NLP? EDIT: I want something similar to what the stanford parser does: Given a sentence "I shot an elephant in my sleep", it should return something like: nsubj(shot-2, I-1) det

问题 I have a .NET application where, given a noun, I want it to correctly prefix that word with "a" or "an". How would I do that? Before you think the answer is to simply check if the first letter is a vowel, consider phrases like: an honest mistake a used car 回答1: Download Wikipedia Unzip it and write a quick filter program that spits out only article text (the download is generally in XML format, along with non-article metadata too). Find all instances of a(n).... and make an index on the

问题 Which ngram implementation is fastest in python? I've tried to profile nltk's vs scott's zip (http://locallyoptimal.com/blog/2013/01/20/elegant-n-gram-generation-in-python/): from nltk.util import ngrams as nltkngram import this, time def zipngram(text,n=2): return zip(*[text.split()[i:] for i in range(n)]) text = this.s start = time.time() nltkngram(text.split(), n=2) print time.time() - start start = time.time() zipngram(text, n=2) print time.time() - start [out] 0.000213146209717 6

问题 Would it be possible to add an ArrayList as the key of HashMap . I would like to keep the frequency count of bigrams. The bigram is the key and the value is its frequency. For each of the bigrams like "he is", I create an ArrayList for it and insert it into the HashMap . But I am not getting the correct output. public HashMap<ArrayList<String>, Integer> getBigramMap(String word1, String word2) { HashMap<ArrayList<String>, Integer> hm = new HashMap<ArrayList<String>, Integer>(); ArrayList

问题 Using NLTK and WordNet, how do I convert simple tense verb into its present, past or past participle form? For example: I want to write a function which would give me verb in expected form as follows. v = 'go' present = present_tense(v) print present # prints "going" past = past_tense(v) print past # prints "went" 回答1: I think what you're looking for is the NodeBox::Linguistics library. It does exactly that: print en.verb.present("gave") >>> give 回答2: With the help of NLTK this can also be

问题 I used NLTK's ne_chunk to extract named entities from a text: my_sent = "WASHINGTON -- In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement." nltk.ne_chunk(my_sent, binary=True) But I can't figure out how to save these entities

问题 What I am striving to complete is a program which reads in a file and will compare each sentence according to the original sentence. The sentence which is a perfect match to the original will receive a score of 1 and a sentence which is the total opposite will receive a 0. All other fuzzy sentences will receive a grade in between 1 and 0. I am unsure which operation to use to allow me to complete this in Python 3. I have included the sample text in which the Text 1 is the original and the

问题 I've tried PorterStemmer and Snowball but both don't work on all words, missing some very common ones. My test words are: " cats running ran cactus cactuses cacti community communities ", and both get less than half right. See also: Stemming algorithm that produces real words Stemming - code examples or open source projects? 回答1: If you know Python, The Natural Language Toolkit (NLTK) has a very powerful lemmatizer that makes use of WordNet. Note that if you are using this lemmatizer for the