I have dataset of more than 5 million of records which has many noise features(words) in it So i thought of doing spell correction and abbreviation handling.
When i googled for spell correction packages in python i got packages like autocorrect, textblob, hunspell etc and Peter norvig's method
Below is the sample of my dataset
Id description
1 switvch for air conditioner..............
2 control tfrmr...........
3 coling pad.................
4 DRLG machine
5 hair smothing kit...............
I Tried spell correction function by above packages using the code
dataset['description']=dataset['description'].apply(lambda x: list(set([spellcorrection_function(item) for item in x])))
For entire dataset it took more than 12 hours to complete spell correction and also it introduces few noise( for 20% of total words which are important)
for eg: In last row, "smothing" corrected as "something" but it should be "smoothing" ( i dont get "something" in this context)
Approaching Further
When I observed the dataset not all time the spelling of word is wrong, there were also correct instance of spelling somewhere in dataset.So I tokenize the entire dataset and split correct words and wrong words by using dictionary , applied jarowinkler similarity method between all pair of words and selected pairs which is having similarity value 0.93 and more
Wrong word correct word similarity score
switvch switch 0.98
coling cooling 0.98
smothing smoothing 0.99
I got more than 50k pair of similar words which I put in dictionary with wrong word as key and correct word as value
I also kept words with its abbreviation list( ~3k pairs) in dictionary
key value
tfrmr transformer
drlg drilling
Search and replace key-value pair using code
dataset['description']=dataset['description'].replace(similar_word_dictionary,regex=true)
dataset['description']=dataset['description'].replace(abbreviation_dictionary,regex=true)
This code took more than a day to complete for only 10% of my entire dataset which I found is not efficient one.
Along With Python packages I had also found deep spelling which is something very efficient way of doing spelling correction.There was a very clear explanation of RNN-LSTM as spell checker.
As I dont know much about RNN and LSTM i got very basic understanding of above link.
Question
I am confused how to consider trainset for RNN to my problem,
whether
I need to consider correct words ( without any spelling mistake) in entire dataset as trainset and entire description of my dataset as testset.
or Pair of similar words and abbrievation list as trainset and description of my dataset as testset ( where model find wrong word in description and correct it)
or any other way? could some one please tell me how can I approach further
Could you give some more information about the model you are building?
It makes sense to use a character level sequence to sequence model, similar to the one you would use for translation. There are already some approaches trying to do the same (1, 2, 3).
Maybe draw on them for some inspiration?
Now, with regards to the dataset, It seems that the one you are trying to use mostly has errors? If you don't have the correct version of each phrase, I don't think you can use this dataset.
A simple approach would be to get an existing dataset and introduce random noise in it. The deep spelling blog talks about how you can do that an existing text corpus. Also, a recommendation from myself would be to use small-ish standalone sentences as the training set. A good place to find those is from machine translation datasets (like the tatoeba project) and only use the english phrases. Out of those you can create pairs of (input_phrase, target_phrase) where the input_phrase is potentially noisy (but not always).
With regards to performance, firstly 12hrs training for 1 pass of a 5M dataset sounds about right for a home pc. You can use a GPU or a cloud solution (1, 2) for faster training.
Now for false-positive correction, the dictionary you have created could indeed be handy: if a word exists in this dictionary, don't accept a "correction" on it from the model.
Related
I have various restaurant labels with me and i have some words that are unrelated to restaurants as well. like below:
vegan
vegetarian
pizza
burger
transportation
coffee
Bookstores
Oil and Lube
I have such mix of around 500 labels. I want to know is there a way pick the similar labels that are related to food choices and leave out words like oil and lube, transportation.
I tried using word2vec but, some of them have more than one word and could not figure out a right way.
Brute-force approach is to tag them manually. But, i want to know is there a way using NLP or Word2Vec to cluster all related labels together.
Word2Vec could help with this, but key factors to consider are:
How are your word-vectors trained? Using off-the-shelf vectors (like say the popular GoogleNews vectors trained on a large corpus of news stories) are unlikely to closely match the senses of these words in your domain, or include multi-word tokens like 'oil_and_lube'. But, if you have a good training corpus from your own domain, with multi-word tokens from a controlled vocabulary (like oil_and_lube) that are used in context, you might get quite good vectors for exactly the tokens you need.
The similarity of word-vectors isn't strictly 'synonymity' but often other forms of close-relation including oppositeness and other ways words can be interchangeable or be used in similar contexts. So whether or not the word-vector similarity-values provide a good threshold cutoff for your particular desired "related to food" test is something you'd have to try out & tinker around. (For example: whether words that are drop-in replacements for each other are closest to each other, or words that are common-in-the-same-topics are closest to each other, can be influenced by whether the window parameter is smaller or larger. So you could find tuning Word2Vec training parameters improve the resulting vectors for your specific needs.)
Making more recommendations for how to proceed would require more details on the training data you have available – where do these labels come from? what's the format they're in? how much do you have? – and your ultimate goals – why is it important to distinguish between restaurant- and non-restaurant- labels?
OK, thank you for the details.
In order to train on word2vec you should take into account the following facts :
You need a huge and variate text dataset. Review your training set and make sure it contains the useful data you need in order to obtain what you want.
Set one sentence/phrase per line.
For preprocessing, you need to delete punctuation and set all strings to lower case.
Do NOT lemmatize or stemmatize, because the text will be less complex!
Try different settings:
5.1 Algorithm: I used word2vec and I can say BagOfWords (BOW) provided better results, on different training sets, than SkipGram.
5.2 Number of layers: 200 layers provide good result
5.3 Vector size: Vector length = 300 is OK.
Now run the training algorithm. The, use the obtained model in order to perform different tasks. For example, in your case, for synonymy, you can compare two words (i.e. vectors) with cosine (or similarity). From my experience, cosine provides a satisfactory result: the distance between two words is given by a double between 0 and 1. Synonyms have high cosine values, you must find the limit between words which are synonyms and others that are not.
Folks,
I have searched Google for different type of papers/blogs/tutorials etc but haven't found anything helpful. I would appreciate if anyone can help me. Please note that I am not asking for code step-by-step but rather an idea/blog/paper or some tutorial.
Here's my problem statement:
Just like sentiment analysis is used for identifying positive and
negative tone of a sentence, I want to find whether a sentence is
forward-looking (future outlook) statement or not.
I do not want to use bag of words approach to sum up the number of forward-looking words/phrases such as "going forward", "in near future" or "In 5 years from now" etc. I am not sure if word2vec or doc2vec can be used. Please enlighten me.
Thanks.
It seems what you are interested in doing is finding temporal statements in texts.
Not sure of your final output, but let's assume you want to find temporal phrases or sentences which contain them.
One methodology could be the following:
Create list of temporal terms [days, years, months, now, later]
Pick only sentences with key terms
Use sentences in doc2vec model
Infer vector and use distance metric for new sentence
GMM Cluster + Limit
Distance from average
Another methodology could be:
Create list of temporal terms [days, years, months, now, later]
Do Bigram and Trigram collocation extraction
Keep relevant collocations with temporal terms
Use relevant collocations in a kind of bag-of-collocations approach
Matched binary feature vectors for relevant collocations
Train classifier to recognise higher level text
This sounds like a good case for a Bootstrapping approach if you have large amounts of texts.
Both are semi-supervised really, since there is some need for finding initial temporal terms, but even that could be automated using a word2vec scheme and bootstrapping
I'm trying to perform document classification into two categories (category1 and category2), using Weka.
I've gathered a training set consisting of 600 documents belonging to both categories and the total number of documents that are going to be classified is 1,000,000.
So to perform the classification, I apply the StringToWordVector filter. I set true the followings from the filter:
- IDF transform
- TF ransform
- OutputWordCounts
I'd like to ask a few questions about this process.
1) How many documents shall I use as training set, so that I over-fitting is avoided?
2) After applying the filter, I get a list of the words in the training set. Do I have to remove any of them to get a better result at the classifier or it doesn't play any role?
3) As classification method I usually choose naiveBayes but the results I get are the followings:
-------------------------
Correctly Classified Instances 393 70.0535 %
Incorrectly Classified Instances 168 29.9465 %
Kappa statistic 0.415
Mean absolute error 0.2943
Root mean squared error 0.5117
Relative absolute error 60.9082 %
Root relative squared error 104.1148 %
----------------------------
and if I use SMO the results are:
------------------------------
Correctly Classified Instances 418 74.5098 %
Incorrectly Classified Instances 143 25.4902 %
Kappa statistic 0.4742
Mean absolute error 0.2549
Root mean squared error 0.5049
Relative absolute error 52.7508 %
Root relative squared error 102.7203 %
Total Number of Instances 561
------------------------------
So in document classification which one is "better" classifier?
Which one is better for small data sets, like the one I have?
I've read that naiveBayes performs better with big data sets but if I increase my data set, will it cause the "over-fitting" effect?
Also, about Kappa statistic, is there any accepted threshold or it doesn't matter in this case because there are only two categories?
Sorry for the long post, but I've been trying for a week to improve the classification results with no success, although I tried to get documents that fit better in each category.
1) How many documents shall I use as training set, so that I
over-fitting is avoided? \
You don't need to choose the size of training set, in WEKA, you just use the 10-fold cross-validation. Back to the question, machine learning algorithms influence much more than data set in over-fitting problem.
2) After applying the filter, I get a list of the words in the
training set. Do I have to remove any of them to get a better result
at the classifier or it doesn't play any role? \
Definitely it does. But whether the result get better can not be promised.
3) As classification method I usually choose naiveBayes but the
results I get are the followings: \
Usually, to define whether a classify algorithm is good or not, the ROC/AUC/F-measure value is always considered as the most important indicator. You can learn them in any machine learning book.
To answers your questions:
I would use (10 fold) cross-validation to evaluate your method. The model is trained trained 10 times on 90% of the data and tested on 10% of the data using different parts of the data each time. The results are therefor less biased towards your current (random) selection of train and test set.
Removing stop words (i.e., frequently occurring words with little discriminating value like the, he or and) is a common strategy to improve your classifier. Weka's StringToWordVector allows you to select a file containing these stop words, but it should also have a default list with English stop words.
Given your results, SMO performs the best of the two classifiers (e.g., it has more Correctly Classified Instances). You might also want to take a look at (Lib)SVM or LibLinear (You may need to install them if they are not in Weka natively; Weka 3.7.6 has a package manager allowing for easy installation), which can perform quite well on document classification as well.
Regarding the second question
2) After applying the filter, I get a list of the words in the training set. Do I have to remove any of them to get a better result at the classifier or it doesn't play any role?
I was building a classifier and training it with the famous 20news group dataset, when testing it without the preprocessing the results were not good. So, i pre-processed the data according to the following steps:
Substitute TAB, NEWLINE and RETURN characters by SPACE.
Keep only letters (that is, turn punctuation, numbers, etc. into SPACES).
Turn all letters to lowercase.
Substitute multiple SPACES by a single SPACE.
The title/subject of each document is simply added in the beginning of the document's text.
no-short Obtained from the previous file, by removing words that are less than 3 characters long. For example, removing "he" but keeping "him".
no-stop Obtained from the previous file, by removing the 524 SMART stopwords. Some of them had already been removed, because they were shorter than 3 characters.
stemmed Obtained from the previous file, by applying Porter's Stemmer to the remaining
words. Information about stemming can be found here.
These steps are taken from http://web.ist.utl.pt/~acardoso/datasets/
I am trying to do document classification using Support Vector Machines (SVM). The documents I have are collection of emails. I have around 3000 documents to train the SVM classifier and have a test document set of around 700 for which I need classification.
I initially used binary DocumentTermMatrix as the input for SVM training. I got around 81% accuracy for the classification with the test data. DocumentTermMatrix was used after removing several stopwords.
Since I wanted to improve the accuracy of this model, I tried using LSA/SVD based dimensional reduction and use the resulting reduced factors as input to the classification model (I tried with 20, 50, 100 and 200 singular values from the original bag of ~ 3000 words). The performance of the classification worsened in each case. (Another reason for using LSA/SVD was to overcome memory issues with one of the response variable that had 65 levels).
Can someone provide some pointers on how to improve the performance of LSA/SVD classification? I realize this is general question without any specific data or code but would appreciate some inputs from the experts on where to start the debugging.
FYI, I am using R for doing the text preprocessing (packages: tm, snowball,lsa) and building classification models (package: kernelsvm)
Thank you.
Here's some general advice - nothing specific to LSA, but it might help improving the results nonetheless.
'binary documentMatrix' seems to imply your data is represented by binary values, i.e. 1 for a term existing in a document, and 0 for non-existing term; moving to other scoring scheme
(e.g. tf/idf) might lead to better results.
LSA is a good metric for dimensional reduction in some cases, but less so in others. So depending in the exact nature of your data, it might be a good idea to consider additional methods, e.g. Infogain.
If the main incentive for reducing the dimensionality is the one parameter with 65 levels, maybe treating this parameter specifically, e.g. by some form of quantization, would lead to a better tradeoff?
This might not be the best tailored answer. Hope these suggestions may help.
Maybe you could use lemmatization over stemming to reduce unacceptable outcomes.
Short and dense: http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html
The goal of both stemming and lemmatization is to reduce inflectional forms and
sometimes derivationally related forms of a word to a common base form.
However, the two words differ in their flavor. Stemming usually refers to a crude
heuristic process that chops off the ends of words in the hope of achieving this
goal correctly most of the time, and often includes the removal of derivational
affixes. Lemmatization usually refers to doing things properly with the use of a
vocabulary and morphological analysis of words, normally aiming to remove
inflectional endings only and to return the base or dictionary form of a word,
which is known as the lemma.
One instance:
go,goes,going ->Lemma: go,go,go ||Stemming: go, goe, go
And use some predefined set of rules; such that short term words are generalized. For instance:
I'am -> I am
should't -> should not
can't -> can not
How to deal with parentheses inside a sentence.
This is a dog(Its name is doggy)
Text inside parentheses often referred to alias names of the entities mentioned. You can either removed them or do correference analysis and treat it as a new sentence.
Try to use Local LSA, which can improve the classification process compared to Global LSA. In addition, LSA's power depends entirely on its parameters, so try to tweak parameters (start with 1, then 2 or more) and compare results to enhance the performance.
I am embarking upon a NLP project for sentiment analysis.
I have successfully installed NLTK for python (seems like a great piece of software for this). However,I am having trouble understanding how it can be used to accomplish my task.
Here is my task:
I start with one long piece of data (lets say several hundred tweets on the subject of the UK election from their webservice)
I would like to break this up into sentences (or info no longer than 100 or so chars) (I guess i can just do this in python??)
Then to search through all the sentences for specific instances within that sentence e.g. "David Cameron"
Then I would like to check for positive/negative sentiment in each sentence and count them accordingly
NB: I am not really worried too much about accuracy because my data sets are large and also not worried too much about sarcasm.
Here are the troubles I am having:
All the data sets I can find e.g. the corpus movie review data that comes with NLTK arent in webservice format. It looks like this has had some processing done already. As far as I can see the processing (by stanford) was done with WEKA. Is it not possible for NLTK to do all this on its own? Here all the data sets have already been organised into positive/negative already e.g. polarity dataset http://www.cs.cornell.edu/People/pabo/movie-review-data/ How is this done? (to organise the sentences by sentiment, is it definitely WEKA? or something else?)
I am not sure I understand why WEKA and NLTK would be used together. Seems like they do much the same thing. If im processing the data with WEKA first to find sentiment why would I need NLTK? Is it possible to explain why this might be necessary?
I have found a few scripts that get somewhat near this task, but all are using the same pre-processed data. Is it not possible to process this data myself to find sentiment in sentences rather than using the data samples given in the link?
Any help is much appreciated and will save me much hair!
Cheers Ke
The movie review data has already been marked by humans as being positive or negative (the person who made the review gave the movie a rating which is used to determine polarity). These gold standard labels allow you to train a classifier, which you could then use for other movie reviews. You could train a classifier in NLTK with that data, but applying the results to election tweets might be less accurate than randomly guessing positive or negative. Alternatively, you can go through and label a few thousand tweets yourself as positive or negative and use this as your training set.
For a description of using Naive Bayes for sentiment analysis with NLTK: http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/
Then in that code, instead of using the movie corpus, use your own data to calculate word counts (in the word_feats method).
Why dont you use WSD. Use Disambiguation tool to find senses. and use map polarity to the senses instead of word. In this case you will get a bit more accurate results as compared to word index polarity.