Objective: a node.js function that can be passed a news article (title, text, tags, etc.) and will return a category for that article ("Technology", "Fashion", "Food", etc.)
I'm not picky about exactly what categories are returned, as long as the list of possible results is finite and reasonable (10-50).
There are Web APIs that do this (eg, alchemy), but I'd prefer not to incur the extra cost (both in terms of external HTTP requests and also $$) if possible.
I've had a look at the node module "natural". I'm a bit new to NLP, but it seems like maybe I could achieve this by training a BayesClassifier on a reasonable word list. Does this seem like a good/logical approach? Can you think of anything better?
I don't know if you are still looking for an answer, but let me put my two cents for anyone who happens to come back to this question.
Having worked in NLP i would suggest you look into the following approach to solve the problem.
Don't look for a single package solution. There are great packages out there, no doubt for lots of things. But when it comes to active research areas like NLP, ML and optimization, the tools tend to be atleast 3 or 4 iterations behind whats there is academia.
Coming to the core problem. What you want to achieve is text classification.
The simplest way to achieve this would be an SVM multiclass classifier.
Simplest yes, but also with very very (see the double stress) reasonable classification accuracy, runtime performance and ease of use.
The thing which you would need to work on would be the feature set used to represent your news article/text/tag. You could use a bag of words model. add named entities as additional features. You can use article location/time as features. (though for a simple category classification this might not give you much improvement).
The bottom line is. SVM works great. they have multiple implementations. and during runtime you don't really need much ML machinery.
Feature engineering on the other hand is very task specific. But given some basic set of features and a good labelled data you can train a very decent classifier.
here are some resources for you.
http://svmlight.joachims.org/
SVM multiclass is what you would be interested in.
And here is a tutorial by SVM zen himself!
http://www.cs.cornell.edu/People/tj/publications/joachims_98a.pdf
I don't know about the stability of this but from the code its a binary classifier SVM. which means if you have a known set of tags of size N you want to classify the text into, you will have to train N binary SVM classifiers. One each for the N category tags.
Hope this helps.
Related
I have some 360 odd features on which I am training my neural network model.
The accuracy I am getting is abysmally bad. There is one feature amongst the 360 that is more important than the others.
Right now, it does not enjoy any special status amongst the other features.
Is there a way to lay emphasis on one of the features while training the model? I believe this could improve my model's accuracy.
I am using Python 3.5 with Keras and Scikit-learn.
EDIT: I am attempting a regression problem
Any help would be appreciated
First of all, I would make sure that this feature alone has a decent prediction probability, but I am assuming that you already made sure of it.
Then, one approach that you could take, is to "embed" your 359 other features in a first layer, and only feed in your special feature once you have compressed the remaining information.
Contrary to what most tutorials make you believe, you do not have to add in all features already in the first layer, but can technically insert them at any point in time (or even multiple times).
The first layer that captures your other inputs is then some form of "PCA approximator", where you are embedding a high-dimensional feature space (359 dimensions) into something that is less dominant over your other feature (maybe 20-50 dimensions as a starting point?)
Of course there is no guarantee that this will work, but you might have a much better chance of getting attention on your special feature, although I am fairly certain that in general you should still see an increase in performance if the single feature is strongly enough correlated with your output.
The other question that is still open is the kind of task you are training for, i.e., whether you are doing some form of classification (if so, how many classes?), or regression. This might also influence architectural choices, and the amount of focus you can/should put on a single feature.
There are several feature selection and importance techniques in machine learning. Please follow this link.
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'm working on a data mining project and would like to mine this dataset Higher Education Enrolments for interesting patterns or knowledge. My problem is figuring out which technique would work best for the dataset.
I'm currently working on the dataset using RapidMiner 5.0 and I removed two columns (E550 - Reference year, E931 - Total Student EFTSL) from the data as they would not be relevant to the analysis. The rest of the attributes are nominal except StudentID (integer) which I have used as my id. I'm currently using classification on it (Naive Bayes) but would like to get the opinion of others, hopefully those who have had more experience in this area. Thanks.
The best technique depends on many factors: type/distribution of training and target attribute, domain, value range of attributes, etc. The best technique to use is the result of data analysis and understanding.
In this particular case, you should clarify which is the attribute to predict.
Unless you already know what you are looking for, and know about the quality of the data source, you should always start by trying various exploratory analysis:
look at some of the first and second order statistics of all the
variables
generate histograms of each variable, to get an idea of the empirical
distribution of each
take a look at pairwise scatter plots of variables that might have
dependency
try other visualization that you might think of
These would give you a rough idea about what kind of pattern might be present and might be discoverable given the noise level. Then depending on what kind of pattern you are interested in, you could start trying various unsupervised pattern learning methods such as, PCA/ICA/factor analysis, clustering, or supervised methods, such as regression, classification.
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.
I want to classify text messages into several categories like, "relation building", "coordination", "information sharing", "knowledge sharing" & "conflict resolution". I am using NLTK library to process these data. I would like to know which classifier, in nltk, is better for this particular multi-class classification problem.
I am planning to use Naive Bayes Classification, is it advisable?
Naive Bayes is the simplest and easy to understand classifier and for that reason it's nice to use. Decision Trees with a beam search to find the best classification are not significantly harder to understand and are usually a bit better. MaxEnt and SVM tend be more complex, and SVM requires some tuning to get right.
Most important is the choice of features + the amount/quality of data you provide!
With your problem, I would focus first on ensuring you have a good training/testing dataset and also choose good features. Since you are asking this question you haven't had much experience with machine learning for NLP, so I'd say start of easy with Naive Bayes as it doesn't use complex features- you can just tokenize and count word occurrences.
EDIT:
The question How do you find the subject of a sentence? and my answer are also worth looking at.
Yes, Training a Naive Bayes Classifier for each category and then labeling each message to a class based on which Classifier provides the highest score is a standard first approach to problems like this. There are more sophisticated single class classifier algorithms which you could substitute in for Naive Bayes if you find performance inadequate, such as a Support Vector Machine ( Which I believe is available in NLTK via a Weka plug in, but not positive). Unless you can think of anything specific in this problem domain that would make Naieve Bayes especially unsuitable, its ofen the go-to "first try" for a lot of projects.
The other NLTK classifier I would consider trying would be MaxEnt as I believe it natively handles multiclass classification. (Though the multiple binary classifer approach is very standard and common as well). In any case the most important thing is to collect a very large corpus of properly tagged text messages.
If by "Text Messages" you are referring to actual cell phone text messages these tend to be very short and the language is very informal and varied, I think feature selection may end up being a larger factor in determining accuracy than classifier choice for you. For example, using a Stemmer or Lemmatizer that understands common abbreviations and idioms used, tagging part of speech or chunking , entity extraction, extracting probably relationships between terms may provide more bang than using more complex classifiers.
This paper talks about classifying Facebook status messages based on sentiment, which has some of the same issues, and may provide some insights into this. The links is to a google cache because I'm having problems w/ the original site:
http://docs.google.com/viewer?a=v&q=cache:_AeBYp6i1ooJ:nlp.stanford.edu/courses/cs224n/2010/reports/ssoriajr-kanej.pdf+maxent+classifier+multiple+classes&hl=en&gl=us&pid=bl&srcid=ADGEESi-eZHTZCQPo7AlcnaFdUws9nSN1P6X0BVmHjtlpKYGQnj7dtyHmXLSONa9Q9ziAQjliJnR8yD1Z-0WIpOjcmYbWO2zcB6z4RzkIhYI_Dfzx2WqU4jy2Le4wrEQv0yZp_QZyHQN&sig=AHIEtbQN4J_XciVhVI60oyrPb4164u681w&pli=1