Text Classification basically works on the input training sentences. Little or less number of variations of in the sentences do work. But when there is a scenario like
What can you do <<==>> What are your capabilities
This scenario does not work well with the regular classification or bot building platforms.
Are there any approaches for classification that would help me achieve this ?
What you are trying to solve is called Semantic Textual Similarity and is a known and well studied field.
There are many different ways to solve this even if your data is tagged or not.
For example, Google has published the Universal Sentence Encoder (code example) which is intended to tell if two sentences are similar like in your case.
Another example would be any solution you can find in Quora Question Pairs Kaggle competition.
There are also datasets for this problem, for example you can look for SemEval STS (STS for Semantic Textual Similarity), or the PAWS dataset
Related
I am building an NLP pipeline and I am trying to get my head around in regards to the optimal structure. My understanding at the moment is the following:
Step1 - Text Pre-processing [a. Lowercasing, b. Stopwords removal, c. stemming, d. lemmatisation,]
Step 2 - Feature extraction
Step 3 - Classification - using the different types of classifier(linearSvC etc)
From what I read online there are several approaches in regard to feature extraction but there isn't a solid example/answer.
a. Is there a solid strategy for feature extraction ?
I read online that you can do [a. Vectorising usin ScikitLearn b. TF-IDF]
but also I read that you can use Part of Speech or word2Vec or other embedding and Name entity recognition.
b. What is the optimal process/structure of using these?
c. On the text pre-processing I am ding the processing on a text column on a df and the last modified version of it is what I use as an input in my classifier. If you do feature extraction do you do that in the same column or you create a new one and you only send to the classifier the features from that column?
Thanks so much in advance
The preprocessing pipeline depends mainly upon your problem which you are trying to solve. The use of TF-IDF, word embeddings etc. have their own restrictions and advantages.
You need to understand the problem and also the data associated with it. In order to make the best use of the data, we need to implement the proper pipeline.
Specifically for text related problems, you will find word embeddings to be very useful. TF-IDF is useful when the problem needs to be solved emphasising the words with lesser frequency. Word embeddings, on the other hand, convert the text to a N-dimensional vector which may show up similarity with some other vector. This could bring a sense of association in your data and the model can learn the best features possible.
In simple cases, we can use a bag of words representation to tokenize the texts.
So, you need to discover the best approach for your problem. If you are solving a problems which closely resembles the famous NLP problems like IMDB review classification, sentiment analysis on Twitter data, then you can find a number of approaches on the internet.
I am given a task of classifying a given news text data into one of the following 5 categories - Business, Sports, Entertainment, Tech and Politics
About the data I am using:
Consists of text data labeled as one of the 5 types of news statement (Bcc news data)
I am currently using NLP with nltk module to calculate the frequency distribution of every word in the training data with respect to each category(except the stopwords).
Then I classify the new data by calculating the sum of weights of all the words with respect to each of those 5 categories. The class with the most weight is returned as the output.
Heres the actual code.
This algorithm does predict new data accurately but I am interested to know about some other simple algorithms that I can implement to achieve better results. I have used Naive Bayes algorithm to classify data into two classes (spam or not spam etc) and would like to know how to implement it for multiclass classification if it is a feasible solution.
Thank you.
In classification, and especially in text classification, choosing the right machine learning algorithm often comes after selecting the right features. Features are domain dependent, require knowledge about the data, but good quality leads to better systems quicker than tuning or selecting algorithms and parameters.
In your case you can either go to word embeddings as already said, but you can also design your own custom features that you think will help in discriminating classes (whatever the number of classes is). For instance, how do you think a spam e-mail is often presented ? A lot of mistakes, syntaxic inversion, bad traduction, punctuation, slang words... A lot of possibilities ! Try to think about your case with sport, business, news etc.
You should try some new ways of creating/combining features and then choose the best algorithm. Also, have a look at other weighting methods than term frequencies, like tf-idf.
Since your dealing with words I would propose word embedding, that gives more insights into relationship/meaning of words W.R.T your dataset, thus much better classifications.
If you are looking for other implementations of classification you check my sample codes here , these models from scikit-learn can easily handle multiclasses, take a look here at documentation of scikit-learn.
If you want a framework around these classification that is easy to use you can check out my rasa-nlu, it uses spacy_sklearn model, sample implementation code is here. All you have to do is to prepare the dataset in a given format and just train the model.
if you want more intelligence then you can check out my keras implementation here, it uses CNN for text classification.
Hope this helps.
I am trying to build a classifier to detect subjectivity. I have text files tagged with subjective and objective . I am little lost with the concept of features creation from this data. I have found the lexicon of the subjective and objective tag. One thing that I can do is to create a feature of having words present in respective dictionary. Maybe the count of words present in subjective and objective dictionary. After that I intend to use naive bayes or SVM to develop the model
My problem is as follow
Is my approach correct ?
Can I create more features ? If possible suggest some or point me to some paper or link
Can I do some test like chi -sq etc to identify effective words from the dictionary ?
You are basically on the right track. I would try and apply classifier with features you already have and see how well it will work, before doing anything else.
Actually best way to improve your work is to google for subjectivity classification papers and read them (there are a quite a number of them). For example this one lists typical features for this task.
And yes Chi-squared can be used to construct dictionaries for text classification (other commonly used methods are TD*IDF, pointwise mutal information and LDA)
Also, recently new neural network-based methods for text classification such as paragraph vector and dynamic convolutional neural networks with k-max pooling demonstrated state-of-the-art results on sentiment analysis, thus they should probably be good for subjectivity classification as well.
So, this question might be a little naive, but I thought asking the friendly people of Stackoverflow wouldn't hurt.
My current company has been using a third party API for NLP for a while now. We basically URL encode a string and send it over, and they extract certain entities for us (we have a list of entities that we're looking for) and return a json mapping of entity : sentiment. We've recently decided to bring this project in house instead.
I've been studying NLTK, Stanford NLP and lingpipe for the past 2 days now, and can't figure out if I'm basically reinventing the wheel doing this project.
We already have massive tables containing the original unstructured text and another table containing the extracted entities from that text and their sentiment. The entities are single words. For example:
Unstructured text : Now for the bed. It wasn't the best.
Entity : Bed
Sentiment : Negative
I believe that implies we have training data (unstructured text) as well as entity and sentiments. Now how I can go about using this training data on one of the NLP frameworks and getting what we want? No clue. I've sort of got the steps, but not sure:
Tokenize sentences
Tokenize words
Find the noun in the sentence (POS tagging)
Find the sentiment of that sentence.
But that should fail for the case I mentioned above since it talks about the bed in 2 different sentences?
So the question - Does any one know what the best framework would be for accomplishing the above tasks, and any tutorials on the same (Note: I'm not asking for a solution). If you've done this stuff before, is this task too large to take on? I've looked up some commercial APIs but they're absurdly expensive to use (we're a tiny startup).
Thanks stackoverflow!
OpenNLP may also library to look at. At least they have a small tutuorial to train the name finder and to use the document categorizer to do sentiment analysis. To trtain the name finder you have to prepare training data by taging the entities in your text with SGML tags.
http://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html#tools.namefind.training
NLTK provides a naive NER tagger along with resources. But It doesnt fit into all cases (including finding dates.) But NLTK allows you to modify and customize the NER Tagger according to the requirement. This link might give you some ideas with basic examples on how to customize. Also if you are comfortable with scala and functional programming this is one tool you cannot afford to miss.
Cheers...!
I have discovered spaCy lately and it's just great ! In the link you can find comparative for performance in term of speed and accuracy compared to NLTK, CoreNLP and it does really well !
Though to solve your problem task is not a matter of a framework. You can have two different system, one for NER and one for Sentiment and they can be completely independent. The hype these days is to use neural network and if you are willing too, you can train a recurrent neural network (which has showed best performance for NLP tasks) with attention mechanism to find the entity and the sentiment too.
There are great demo everywhere on the internet, the last two I have read and found interesting are [1] and [2].
Similar to Spacy, TextBlob is another fast and easy package that can accomplish many of these tasks.
I use NLTK, Spacy, and Textblob frequently. If the corpus is simple, generic, and straightforward, Spacy and Textblob work well OOTB. If the corpus is highly customized, domain-specific, messy (incorrect spelling or grammar), etc. I'll use NLTK and spend more time customizing my NLP text processing pipeline with scrubbing, lemmatizing, etc.
NLTK Tutorial: http://www.nltk.org/book/
Spacy Quickstart: https://spacy.io/usage/
Textblob Quickstart: http://textblob.readthedocs.io/en/dev/quickstart.html
Background
For years I've been using my own Bayesian-like methods to categorize new items from external sources based on a large and continually updated training dataset.
There are three types of categorization done for each item:
30 categories, where each item must belong to one category, and at most two categories.
10 other categories, where each item is only associated with a category if there is a strong match, and each item can belong to as many categories as match.
4 other categories, where each item must belong to only one category, and if there isn't a strong match the item is assigned to a default category.
Each item consists of English text of around 2,000 characters. In my training dataset there are about 265,000 items, which contain a rough estimate of 10,000,000 features (unique three word phrases).
My homebrew methods have been fairly successful, but definitely have room for improvement. I've read the NLTK book's chapter "Learning to Classify Text", which was great and gave me a good overview of NLP classification techniques. I'd like to be able to experiment with different methods and parameters until I get the best classification results possible for my data.
The Question
What off-the-shelf NLP tools are available that can efficiently classify such a large dataset?
Those I've tried so far:
NLTK
TIMBL
I tried to train them with a dataset that consisted of less than 1% of the available training data: 1,700 items, 375,000 features. For NLTK I used a sparse binary format, and a similarly compact format for TIMBL.
Both seemed to rely on doing everything in memory, and quickly consumed all system memory. I can get them to work with tiny datasets, but nothing large. I suspect that if I tried incrementally adding the training data the same problem would occur either then or when doing the actual classification.
I've looked at Google's Prediction API, which seem to do much of what I'm looking for but not everything. I'd also like to avoid relying on an external service if possible.
About the choice of features: in testing with my homebrew methods over the years, three word phrases produced by far the best results. Although I could reduce the number of features by using words or two word phrases, that would most likely produce inferior results and would still be a large number of features.
After this post and based on the personal experience, I would recommend Vowpal Wabbit. It is said to have one of the fastest text classification algorithms.
MALLET has a number of classifiers (NB, MaxEnt, CRF, etc). It's written Andrew McCallum's group. SVMLib is another good option, but SVM models typically require a bit more tuning than MaxEnt. Alternatively some sort of online clustering like K-means might not be bad in this case.
SVMLib and MALLET are quite fast (C and Java) once you have your model trained. Model training can take a while though! Unfortunately it's not always easy to find example code. I have some examples of how to use MALLET programmatically (along with the Stanford Parser, which is slow and probably overkill for your purposes). NLTK is a great learning tool and is simple enough that is you can prototype what you are doing there, that's ideal.
NLP is more about features and data quality than which machine learning method you use. 3-grams might be good, but how about character n-grams across those? Ie, all the character ngrams in a 3-gram to account for spelling variations/stemming/etc? Named entities might also be useful, or some sort of lexicon.
I would recommend Mahout as it is intended for handling very large scale data sets.
The ML algorithms are built over Apache Hadoop(map/reduce), so scaling is inherent.
Take a look at classification section below and see if it helps.
https://cwiki.apache.org/confluence/display/MAHOUT/Algorithms
Have you tried MALLET?
I can't be sure that it will handle your particular dataset but I've found it to be quite robust in previous tests of mine.
However, I my focus was on topic modeling rather than classification per se.
Also, beware that with many NLP solutions you needn't input the "features" yourself (as the N-grams, i.e. the three-words-phrases and two-word-phrases mentioned in the question) but instead rely on the various NLP functions to produce their own statistical model.