pos-tagger

Here is the R Code: library(NLP) library(openNLP) tagPOS <- function(x, ...) { s <- as.String(x) word_token_annotator <- Maxent_Word_Token_Annotator() a2 <- Annotation(1L, "sentence", 1L, nchar(s)) a2 <- annotate(s, word_token_annotator, a2) a3 <- annotate(s, Maxent_POS_Tag_Annotator(), a2) a3w <- a3[a3$type == "word"] POStags <- unlist(lapply(a3w$features, `[[`, "POS")) POStagged <- paste(sprintf("%s/%s", s[a3w], POStags), collapse = " ") list(POStagged = POStagged, POStags = POStags)} str <- "this is a the first sentence." tagged_str <- tagPOS(str) Output is : tagged_str $POStagged [1]"this

Scikit-learn has fairly user-friendly python modules for machine learning. I am trying to train an SVM tagger for Natural Language Processing (NLP) where my labels and input data are words and annotation. E.g. Part-Of-Speech tagging, rather than using double/integer data as input tuples [[1,2], [2,0]] , my tuples will look like this [['word','NOUN'], ['young', 'adjective']] Can anyone give an example of how i can use the SVM with string tuples? the tutorial/documentation given here are for integer/double inputs. http://scikit-learn.org/stable/modules/svm.html Most machine learning algorithm

When using the parser or for the matter any of the Annotation in Core NLP, is there a way to access the probability or the margin of error? To put my question into context, I am trying to understand if there is a way programmatically to detect a case of ambiguity. For instance in the sentence below the verb desire is detected as a noun. I would like to be able to know so kind of measure I can access or calculate from the Core NLP APi to tell me there could be an ambiguity. (NP (NP (NNP Whereas)) (, ,) (NP (NNP users) (NN desire) (S (VP (TO to) (VP (VB sell)))))) 来源： https://stackoverflow.com

I am working on a project that requires me to tag tokens using nltk and python. So I wanted to use this. But came up with a few problems. I went through a lot of other already asked questions and other forums but I was still unable to get a soultion to this problem. The problem is when I try to execute the following: from nltk.tag import StanfordPOSTagger st = StanfordPOSTagger('english-bidirectional-distsim.tagger') I get the following: Traceback (most recent call last): `File "<pyshell#13>", line 1, in <module> st = StanfordPOSTagger('english-bidirectional-distsim.tagger')` `File "C:\Users

I'm using the Stanford's CoreNLP Named Entity Recognizer (NER) and Part-of-Speech (POS) tagger in my application. The problem is that my code tokenizes the text beforehand and then I need to NER and POS tag each token. However I was only able to find out how to do that using the command line options but not programmatically. Can someone please tell me how programmatically can I NER and POS tag pretokenized text using Stanford's CoreNLP? Edit: I'm actually using the individual NER and POS instructions. So my code was written as instructed in the tutorials given in the Stanford's NER and POS

I'm using the Stanford Tagger for determining the Parts of Speech. However, I want to get more information out of the text. Is there a possibility to get further information like the tense of the sentence or if it is in active/passive? So far, I'm using the very basic PoS-Tagging approach: List<List<TaggedWord>> taggedUnits = new ArrayList<List<TaggedWord>>(); String input = "This sentence is going to be future. The door was opened."; for (List<HasWord> sentence : MaxentTagger.tokenizeText(new StringReader(input))) { taggedUnits.add(tagger.tagSentence(sentence)); } You can get tense

I am looking for an open source Natural Language Processing library for c/c++ and especially i am interested in Part of speech tagging. Take a look at this POS Tagger list from Stanford . Some of them are language independent and others are targeted at C/C++ or have specific bindings. Not present on that list, but still important in my opinion is Citar , a C++ free software part of speech tagger using a trigram Hidden Markov Model. 来源： https://stackoverflow.com/questions/1805099/c-c-nlp-library