I have to build model for natural language processing. For that i have data in text file and I want to convert that data into data-set for NLP model which predict bad and good in it.
I taken sample of emails data with contains some information and it is in text file.
I want to know how can i use that text file as data-set to my nlp model. please help me out
Related
We build an Speech To Text Application. In this Conversation always in dutch language. But in some cases English and Dutch words are same. At that time how can i train my model.
There are different ways to do the task
Train the model with audio samples of the language Dutch (Belgium or Standard) with related transcript
Without any audio file give the text file of the language to train the model
By default settings can be applied like train and test sampling separation, check the sample count and divide the sets.
create a training file with few sentences (repeated content also acceptable). Train the model with that file. Based on the language priority, the file has to contain Dutch and English related words.
Use the following can help you to create a pronunciation file
How can I convert Text Data into LibSVM file format for training the model for spam classification.
Are SVMFiles already Labeled ?
SVM format is neither required or that useful. It is used in Apache Spark ML example, only because it can be map directly to the required format.
Are SVMFiles already Labeled ?
Not necessarily, but Spark can read only labeled variant.
In practice you should use org.apache.spark.ml.feature tools to extract relevant features from your data.
You can follow the documentation as well as a number of questions on SO.,
How convert string data, like a tweet, into PTB format to train the Stanford Sentiment Analysis tool?
This is not a matter of simply converting from one format to another. As #lenz mentioned, PTB is the output format of a parser -- this means at minimum you need to convert text to a syntactic parse. An automated parser (e.g., Berkeley/Stanford/BLLIP parser) could get you some of the way here, but (1) automatic parsers are likely awful on Twitter text, and (2) if I recall you need binarized parse trees, which means a bit of manipulation of the raw parses.
Moreover, to train a sentiment model, you need to annotate your data with sentiment. That is, for each constituent of the parse tree, you need to say what the sentiment label for the yield of that constituent is. If there were an automatic tool that does this, you wouldn't need to train a new model.
The Stanford CoreNLP package has a java class file for converting the text in PTB format for training.
The class name is BuildBinarizedDataset
I'm working on text classification and I want to use Topic models (LDA).
My corpus consists of at least 24, 000 Persian news documents. each doc in the corpus is in format of (keyword, weight) pairs extracted from the news.
I saw two Java toolkits: mallet and lingpipe.
I've read mallet tutorial on importing the data and it gets data in plain text, not the format that I have. is there any way that I could change it?
Also read a little about the lingpipe, the example from tutorial was using arrays of integers. Is it convenient for large data?
I need to know which implementation of LDA is better for me? Are there any other implementation that suits my data? (in Java)
From the keyword-weight file you can create an artificial text containing the words in random order with the given weights. Run mallet on the so-generated texts to retrieve the topics.
Do guide me along if I am not posting in the right section.
I have some text files for my training data which are unformatted in word documents. They all contain ASCII characters only.
I would like to train a model on the text files using data mining methods.
The text files do have about 300 words in each file on average.
Are there any software that are recommended for me to start on it?
My initial idea is to use all the words in one of the file as training data and the remaining as test data. This is to perform cross fold validation.
However, I have tools such as weka but it does not seem to satisfy my needs as converting to csv files does not seem to be feasible in my case as the text files are separated
I have trying to perform cross validation in such a way that all the words in the training data are considered as features.
You need to use weka StringToWord filter and convert your text files to arff files. After that you can use weka classification algorithms. Watch following video to learn basics.