I'm at a cross roads, ive been using Mahout to classify some documents, and have stumbled across OpenNLP document classifier.
They seem to do very similar things, and i cant figure out if its worth converting what I currently have written in mahout, and provide an OpenNLP implementation instead.
Are there some blatently obvious advantages mahout has over OpenNLP for document classification?
My situation is that I have several hundred thousand news articles, and i only want to extract a subset of them. Mahout does this reasonably well, - im using Naive Bayes for term counting, and then TF-IDF to determine which category the documents fall into. The model is updated as and when new articles are found, so the model is consistently improving over time.
It seems OpenNLP document classifier does something very similar (although i have not tested how accurate it is). - does anyone have experience using both, who can say diffentively why one would be used above the other?
I don't have experience with these two, but while trying to figure out if one of them would make a difference in a personal project, I stumbled upon this blog, and I quote:
Data categorization with OpenNLP is another approach with more accuracy and performance rate as compared to mahout.
You can check the blog post here.
Related
I've written a program to analyze a given piece of text from a website and make conclusory classifications as to its validity. The code basically vectorizes the description (taken from the HTML of a given webpage in real-time) and takes in a few inputs from that as features to make its decisions. There are some more features like the domain of the website and some keywords I've explicitly counted.
The highest accuracy I've been able to achieve is with a RandomForestClassifier, (>90%). I'm not sure what I can do to make this accuracy better except incorporating a more sophisticated model. I tried using an MLP but for no set of hyperparameters does it seem to exceed the previous accuracy. I have around 2000 data points available for training.
Is there any classifier that works best for such projects? Does anyone have any suggestions as to how I can bring about improvements? (If anything needs to be elaborated, I'll do so.)
Any suggestions on how I can improve on this project in general? Should I include the text on a webpage as well? How should I do so? I tried going through a few sites, but the next doesn't seem to be contained in any specific element whereas the description is easy to obtain from the HTML. Any help?
What else can I take as features? If anyone could suggest any creative ideas, I'd really appreciate it.
You can search with keyword NLP. The task you are facing is a hot topic among those study deep learning, and is called natural language processing.
RandomForest is a machine learning algorithm, and probably works quite well. Using other machine learning algorithms might improve your accuracy, or maybe not. If you want to try out other machine learning algorithms that are light, it's fine.
Deep Learning most likely will outperform your current model, and starting with keyword NLP, you'll find out many models, hopefully Word2Vec, Bert, and so on. You can find out all the codes on github.
One tip for you, is to think carefully whether you can train the model or not. Trying to train BERT from scratch is a crazy thing to do for a starter, even for an expert. Try to bring pretrained model and finetune it, or just bring the word vectors.
I hope that this works out.
Dear all I am working on a project in which I have to categories research papers into their appropriate fields using titles of papers. For example if a phrase "computer network" occurs somewhere in then title then this paper should be tagged as related to the concept "computer network". I have 3 million titles of research papers. So I want to know how I should start. I have tried to use tf-idf but could not get actual results. Does someone know about a library to do this task easily? Kindly suggest one. I shall be thankful.
If you don't know categories in advance, than it's not classification, but instead clustering. Basically, you need to do following:
Select algorithm.
Select and extract features.
Apply algorithm to features.
Quite simple. You only need to choose combination of algorithm and features that fits your case best.
When talking about clustering, there are several popular choices. K-means is considered one of the best and has enormous number of implementations, even in libraries not specialized in ML. Another popular choice is Expectation-Maximization (EM) algorithm. Both of them, however, require initial guess about number of classes. If you can't predict number of classes even approximately, other algorithms - such as hierarchical clustering or DBSCAN - may work for you better (see discussion here).
As for features, words themselves normally work fine for clustering by topic. Just tokenize your text, normalize and vectorize words (see this if you don't know what it all means).
Some useful links:
Clustering text documents using k-means
NLTK clustering package
Statistical Machine Learning for Text Classification with scikit-learn and NLTK
Note: all links in this answer are about Python, since it has really powerful and convenient tools for this kind of tasks, but if you have another language of preference, you most probably will be able to find similar libraries for it too.
For Python, I would recommend NLTK (Natural Language Toolkit), as it has some great tools for converting your raw documents into features you can feed to a machine learning algorithm. For starting out, you can maybe try a simple word frequency model (bag of words) and later on move to more complex feature extraction methods (string kernels). You can start by using SVM's (Support Vector Machines) to classify the data using LibSVM (the best SVM package).
The fact, that you do not know the number of categories in advance, you could use a tool called OntoGen. The tool basically takes a set of texts, does some text mining, and tries to discover the clusters of documents. It is a semi-supervised tool, so you must guide the process a little, but it does wonders. The final product of the process is an ontology of topics.
I encourage you, to give it a try.
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning algorithms like decision tree, SVM etc. are applicable. My question is, how should I start to approach such problems, I mean whether to start with decision tree or SVM or some other algorithm or is there is any other approach i.e. how will I decide?
So, I had never heard of Kaggle until reading your post--thank you so much, it looks awesome. Upon exploring their site, I found a portion that will guide you well. On the competitions page (click all competitions), you see Digit Recognizer and Facial Keypoints Detection, both of which are competitions, but are there for educational purposes, tutorials are provided (tutorial isn't available for the facial keypoints detection yet, as the competition is in its infancy. In addition to the general forums, competitions have forums also, which I imagine is very helpful.
If you're interesting in the mathematical foundations of machine learning, and are relatively new to it, may I suggest Bayesian Reasoning and Machine Learning. It's no cakewalk, but it's much friendlier than its counterparts, without a loss of rigor.
EDIT:
I found the tutorials page on Kaggle, which seems to be a summary of all of their tutorials. Additionally, scikit-learn, a python library, offers a ton of descriptions/explanations of machine learning algorithms.
This cheatsheet http://peekaboo-vision.blogspot.pt/2013/01/machine-learning-cheat-sheet-for-scikit.html is a good starting point. In my experience using several algorithms at the same time can often give better results, eg logistic regression and svm where the results of each one have a predefined weight. And test, test, test ;)
There is No Free Lunch in data mining. You won't know which methods work best until you try lots of them.
That being said, there is also a trade-off between understandability and accuracy in data mining. Decision Trees and KNN tend to be understandable, but less accurate than SVM or Random Forests. Kaggle looks for high accuracy over understandability.
It also depends on the number of attributes. Some learners can handle many attributes, like SVM, whereas others are slow with many attributes, like neural nets.
You can shrink the number of attributes by using PCA, which has helped in several Kaggle competitions.
I want to teach myself enough machine learning so that I can, to begin with, understand enough to put to use available open source ML frameworks that will allow me to do things like:
Go through the HTML source of pages
from a certain site and "understand"
which sections form the content,
which the advertisements and which
form the metadata ( neither the
content, nor the ads - for eg. -
TOC, author bio etc )
Go through the HTML source of pages
from disparate sites and "classify"
whether the site belongs to a
predefined category or not ( list of
categories will be supplied
beforhand )1.
... similar classification tasks on
text and pages.
As you can see, my immediate requirements are to do with classification on disparate data sources and large amounts of data.
As far as my limited understanding goes, taking the neural net approach will take a lot of training and maintainance than putting SVMs to use?
I understand that SVMs are well suited to ( binary ) classification tasks like mine, and open source framworks like libSVM are fairly mature?
In that case, what subjects and topics
does a computer science graduate need
to learn right now, so that the above
requirements can be solved, putting
these frameworks to use?
I would like to stay away from Java, is possible, and I have no language preferences otherwise. I am willing to learn and put in as much effort as I possibly can.
My intent is not to write code from scratch, but, to begin with putting the various frameworks available to use ( I do not know enough to decide which though ), and I should be able to fix things should they go wrong.
Recommendations from you on learning specific portions of statistics and probability theory is nothing unexpected from my side, so say that if required!
I will modify this question if needed, depending on all your suggestions and feedback.
"Understanding" in machine learn is the equivalent of having a model. The model can be for example a collection of support vectors, the layout and weights of a neural network, a decision tree, or more. Which of these methods work best really depends on the subject you're learning from and on the quality of your training data.
In your case, learning from a collection of HTML sites, you will like to preprocess the data first, this step is also called "feature extraction". That is, you extract information out of the page you're looking at. This is a difficult step, because it requires domain knowledge and you'll have to extract useful information, or otherwise your classifiers will not be able to make good distinctions. Feature extraction will give you a dataset (a matrix with features for each row) from which you'll be able to create your model.
Generally in machine learning it is advised to also keep a "test set" that you do not train your models with, but that you will use at the end to decide on what is the best method. It is of extreme importance that you keep the test set hidden until the very end of your modeling step! The test data basically gives you a hint on the "generalization error" that your model is making. Any model with enough complexity and learning time tends to learn exactly the information that you train it with. Machine learners say that the model "overfits" the training data. Such overfitted models seem to appear good, but this is just memorization.
While software support for preprocessing data is very sparse and highly domain dependent, as adam mentioned Weka is a good free tool for applying different methods once you have your dataset. I would recommend reading several books. Vladimir Vapnik wrote "The Nature of Statistical Learning Theory", he is the inventor of SVMs. You should get familiar with the process of modeling, so a book on machine learning is definitely very useful. I also hope that some of the terminology might be helpful to you in finding your way around.
Seems like a pretty complicated task to me; step 2, classification, is "easy" but step 1 seems like a structure learning task. You might want to simplify it to classification on parts of HTML trees, maybe preselected by some heuristic.
The most widely used general machine learning library (freely) available is probably WEKA. They have a book that introduces some ML concepts and covers how to use their software. Unfortunately for you, it is written entirely in Java.
I am not really a Python person, but it would surprise me if there aren't also a lot of tools available for it as well.
For text-based classification right now Naive Bayes, Decision Trees (J48 in particular I think), and SVM approaches are giving the best results. However they are each more suited for slightly different applications. Off the top of my head I'm not sure which would suit you the best. With a tool like WEKA you could try all three approaches with some example data without writing a line of code and see for yourself.
I tend to shy away from Neural Networks simply because they can get very very complicated quickly. Then again, I haven't tried a large project with them mostly because they have that reputation in academia.
Probability and statistics knowledge is only required if you are using probabilistic algorithms (like Naive Bayes). SVMs are generally not used in a probabilistic manner.
From the sound of it, you may want to invest in an actual pattern classification textbook or take a class on it in order to find exactly what you are looking for. For custom/non-standard data sets it can be tricky to get good results without having a survey of existing techniques.
It seems to me that you are now entering machine learning field, so I'd really like to suggest to have a look at this book: not only it provides a deep and vast overview on the most common machine learning approaches and algorithms (and their variations) but it also provides a very good set of exercises and scientific paper links. All of this is wrapped in an insightful language starred with a minimal and yet useful compendium about statistics and probability
I am trying to implement a naive bayseian approach to find the topic of a given document or stream of words. Is there are Naive Bayesian approach that i might be able to look up for this ?
Also, i am trying to improve my dictionary as i go along. Initially, i have a bunch of words that map to a topics (hard-coded). Depending on the occurrence of the words other than the ones that are already mapped. And depending on the occurrences of these words i want to add them to the mappings, hence improving and learning about new words that map to topic. And also changing the probabilities of words.
How should i go about doing this ? Is my approach the right one ?
Which programming language would be best suited for the implementation ?
Existing Implementations of Naive Bayes
You would probably be better off just using one of the existing packages that supports document classification using naive Bayes, e.g.:
Python - To do this using the Python based Natural Language Toolkit (NLTK), see the Document Classification section in the freely available NLTK book.
Ruby - If Ruby is more of your thing, you can use the Classifier gem. Here's sample code that detects whether Family Guy quotes are funny or not-funny.
Perl - Perl has the Algorithm::NaiveBayes module, complete with a sample usage snippet in the package synopsis.
C# - C# programmers can use nBayes. The project's home page has sample code for a simple spam/not-spam classifier.
Java - Java folks have Classifier4J. You can see a training and scoring code snippet here.
Bootstrapping Classification from Keywords
It sounds like you want to start with a set of keywords that are known to cue for certain topics and then use those keywords to bootstrap a classifier.
This is a reasonably clever idea. Take a look at the paper Text Classication by Bootstrapping with Keywords, EM and Shrinkage by McCallum and Nigam (1999). By following this approach, they were able to improve classification accuracy from the 45% they got by using hard-coded keywords alone to 66% using a bootstrapped Naive Bayes classifier. For their data, the latter is close to human levels of agreement, as people agreed with each other about document labels 72% of the time.