I want to text classification based on the keywords appear in the text, because I do not have sample data to use naive bayes for text classification.
Example:
my document has some few words as "family, mother , father , children ... " that the categories of document are family.Or "football, tennis, score ... " that the category is sport
What is the best algorithm in this case ?.And is there any api java for this problem?
What you have are feature labels, i.e., labels on features rather than instances. There are a few methods for exploiting these, but usually it is assumed that one has instance labels (i.e., labels on documents) in addition to feature labels. This paradigm is referred to as dual-supervision.
Anyway, I know of at least two ways to learn from labeled features alone. The first is Generalized Expectation Criteria, which penalizes model parameters for diverging from a priori beliefs (e.g., that "moether" ought usually to correlate with "family"). This method has the disadvantage of being somewhat complex, but the advantage of having a nicely packaged, open-source Java implementation in the Mallet toolkit (see here, specifically).
A second option would basically be to use Naive Bayes and give large priors to the known word/class associations -- e.g., P("family"|"mother") = .8, or whatever. All unlabeled words would be assigned some prior, presumably reflecting class distribution. You would then effectively being making decisions only based on the prevalence of classes and the labeled term information. Settles proposed a model like this recently, and there is a web-tool available.
You likely will need an auxillary data set for this. You cannot rely on your data set to convey the information that "dad" and "father" and "husband" have a similar meaning.
You can try to do mine for co-occurrences to detect near-synonyms, but this is not very reliable.
Probably wordnet etc. are a good place to disambiguate such words.
You can download the freebase topic collection: http://wiki.freebase.com/wiki/Topic_API.
Related
I'm looking for a way to use machine learning to correctly classify FAQs that don't fit in with pre-defined classes, and should get lumped into an "other" class.
The problem: in the training dataset contains about 1500 FAQs, with "other" being the largest class (about 250 questions are lumped in this class). These are typically "odd-ball" questions, that are asked very infrequently. However, when I train a model, the "other" class becomes a model favourite, just because of the size and variance compared to other classes. If I now use this model to classify the FAQs without class, a decent amount will be lumped into "other" where they shouldn't.
What I want: a model that classifies questions with the specific classes first, and only lumps it in "other" when it can't find a good hit with the specific classes.
What I've tried: undersample the "other" class. This works OKish, but I think there should be a better solution.
I'll try to use the number of times a FAQ is asked as second predictor (not sure how yet), but I'm looking for any out-of-the-box solutions or pointers. Thanks!
I can suggest two strategies to do this classification (however, it is better to say clusstering since it is an unsupervised learning):
First method: use NLP (nltk for example), to discover n most frequent words in the questions and consider them as the class labels. To do so, you need to create a corpus by integrating all the question, clean the text by removing punctuation, stopwords, digit, mention, hashtags and so on, then tokenise and lemmatise the text, and find the most common tokens. I believe it is better to keep only nouns, and take most common nouns. Besides, you can compute tf–idf, and decide based on that instead.
Second method: use fuzzy techniques to compute the similarities between text. To do so, you can use fuzzywuzzy library that contains several functions for computing the similarities. For your case fuzzywuzzy.token_set_ratio() would be the right choice, since you are comparing two sentences. However, since you have 1500 questions, you have (n * (n-1)) / 2 = 1124250 combination to compute similarity for, which is a lot. To make it efficient, I suggest to use itertools
Hope these help!
I am facing a binary prediction task and have a set of features of which all are categorical. A key challenge is therefore to encode those categorical features to numbers and I was looking for smart ways to do so.
I stumbled over word2vec, which is mostly used for NLP, but I was wondering whether I could use it to encode my variables, i.e. simply take the weights of the neural net as the encoded features.
However, I am not sure, whether it is a good idea since, the context words, which serve as the input features in word2vec are in my case more or less random, in contrast to real sentences which word2vec was originially made for.
Do you guys have any advice, thoughts, recommendations on this?
You should look into entity embedding if you are searching for a way to utilize embeddings for categorical variables.
google has a good crash course on the topic: https://developers.google.com/machine-learning/crash-course/embeddings/categorical-input-data
this is a good paper on arxiv written by a team from a Kaggle competition: https://arxiv.org/abs/1604.06737
It's certainly possible to use the word2vec algorithm to train up 'dense embeddings' for things like keywords, tags, categories, and so forth. It's been done, sometimes beneficially.
Whether it's a good idea in your case will depend on your data & goals – the only way to know for sure is to try it, and evaluate the results versus your alternatives. (For example, if the number of categories is modest from a controlled vocabulary, one-hot encoding of the categories may be practical, and depending on the kind of binary classifier you use downstream, the classifier may itself be able to learn the same sorts of subtle interrelationships between categories that could also otherwise be learned via a word2vec model. On the other hand, if categories are very numerous & chaotic, the pre-step of 'compressing' them into a smaller-dimensional space, where similar categories have similar representational vectors, may be more helpful.)
That such tokens don't quite have the same frequency distributions & surrounding contexts as true natural language text may mean it's worth trying a wider range of non-default training options on any word2vec model.
In particular, if your categories don't have a natural ordering giving rise to meaningful near-neighbors relationships, using a giant window (so all words in a single 'text' are in each others' contexts) may be worth considering.
Recent versions of the Python gensim Word2Vec allow changing a parameter named ns_exponent – which was fixed at 0.75 in many early implementations, but at least one paper has suggested can usefully vary far from that value for certain corpus data and recommendation-like applications.
I'm trying to classify some data using knime with knime-labs deep learning plugin.
I have about 16.000 products in my DB, but I have about 700 of then that I know its category.
I'm trying to classify as much as possible using some DM (data mining) technique. I've downloaded some plugins to knime, now I have some deep learning tools as some text tools.
Here is my workflow, I'll use it to explain what I'm doing:
I'm transforming the product name into vector, than applying into it.
After I train a DL4J learner with DeepMLP. (I'm not really understand it all, it was the one that I thought I got the best results). Than I try to apply the model in the same data set.
I thought I would get the result with the predicted classes. But I'm getting a column with output_activations that looks that gets a pair of doubles. when sorting this column I get some related date close to each other. But I was expecting to get the classes.
Here is a print of the result table, here you can see the output with the input.
In columns selection it's getting just the converted_document and selected des_categoria as Label Column (learning node config). And in Predictor node I checked the "Append SoftMax Predicted Label?"
The nom_produto is the text column that I'm trying to use to predict the des_categoria column that it the product category.
I'm really newbie about DM and DL. If you could get me some help to solve what I'm trying to do would be awesome. Also be free to suggest some learning material about what attempting to achieve
PS: I also tried to apply it into the unclassified data (17,000 products), but I got the same result.
I won't answer with a workflow on this one because it is not going to be a simple one. However, be sure to find the text mining example on the KNIME server, i.e. the one that makes use of the bag of words approach.
The task
Product mapping to categories should be a straight-forward data mining task because the information that explains the target variable is available in a quasi-exhaustive manner. Depending on the number of categories to train though, there is a risk that you might need more than 700 instances to learn from.
Some resources
Here are some resources, only the first one being truly specialised in text mining:
Introduction on Information Retrieval, in particular chapter 13;
Data Science for Business is an excellent introduction to data mining, including text mining (chapter 10), also do not forget the chapter about similarity (chapter 6);
Machine Learning with R has the advantage of being accessible enough (chapter 4 provides an example of text classification with R code).
Preprocessing
First, you will have to preprocess your product labels a bit. Use KNIME's text analytics preprocessing nodes for that purpose, that is after you've transformed the product labels with Strings to Document:
Case Convert, Punctuation Erasure and Snowball Stemmer;
you probably won't need Stop Word Filter, however, there may be quasi-stop words such as "product", which you may need to remove manually with Dictionary Filter;
Be careful not to use any of the following without testing testing their impact first: N Chars Filter (g may be a useful word), Number Filter (numbers may indicate quantities, which may be useful for classification).
Should you encounter any trouble with the relevant nodes (e.g. Punctuation Erasure can be tricky amazingly thanks to the tokenizer), you can always apply String Manipulation with regex before converting the Strings to Document.
Keep it short and simple: the lookup table
You could build a lookup table based on the 700 training instances. The book Data mining techniques as well as resource (2) present this approach in some detail. If any model performs any worse than the lookup table, you should abandon the model.
Nearest neighbors
Neural networks are probably overkill for this task.
Start with a K Nearest Neighbor node (applying a string distance such as Cosine, Levensthein or Jaro-Winkler). This approach requires the least amount of data wrangling. At the very least, it will provide an excellent baseline model, so it is most definitely worth a shot.
You'll need to tune the parameter k and to experiment with the distance types. The Parameter Optimization Loop pair will help you with optimizing k, you can include a Cross-Validation meta node inside of the said loop to obtain an estimate of the expected performance given k instead of only one point estimate per value of k. Use Cohen's Kappa as an optimization criterion, as proposed by the resource number (3) and available via the Scorer node.
After the parameter tuning, you'll have to evaluate the relevance of your model using yet another Cross-Validation meta node, then follow up with a Loop pair including Scorer to calculate the descriptives on performance metric(s) per iteration, finally use Statistics. Kappa is a convenient metric for this task because the target variable consists of many product categories.
Don't forget to test its performance against the lookup table.
What next ?
Should lookup table or k-nn work well for you, then there's nothing else to add.
Should any of those approaches fail, you might want to analyse the precise cases on which it fails. In addition, training set size may be too low, so you could manually classify another few hundred or thousand instances.
If after increasing the training set size, you are still dealing with a bad model, you can try the bag of words approach together with a Naive Bayes classifier (see chapter 13 of the Information Retrieval reference). There is no room here to elaborate on the bag of words approach and Naive Bayes but you'll find the resources here above useful for that purpose.
One last note. Personally, I find KNIME's Naive Bayes node to perform poorly, probably because it does not implement Laplace smoothening. However, KNIME's R Learner and R Predictor nodes will allow you to use R's e1071 package, as demonstrated by resource (3).
I am trying to do document classification. But I am really confused between feature selections and tf-idf. Are they the same or two different ways of doing classification?
Hope somebody can tell me? I am not really sure that my question will make sense to you guys.
Yes, you are confusion a lot of things.
Feature selection is the abstract term for choosing features (0 or 1). Stopword removal can be seen as feature selection.
TF is one method of extracting features from text: counting words.
IDF is one method of assigning weights to features.
Neither of them is classification... they are popular for text classification, but they are even more popular for information retrieval, which is not classification...
However, many classifiers work on numeric data, so the common process is to 1. Extract features (e.g.: TF) 2. Select features (e.g. remove stopwords) 3. Weight features (e.g. IDF) 4. Train a classifier on the resulting numerical vectors. 5. Predict the classes of new/unlabeled documents.
Taking a look at this explanation may help a lot when it comes to understanding text classifiers.
TF-IDF is a good way to find a document that answers a given query, but it does not necessarily assigns documents with classes.
Examples that may be helpful:
1) You have a bunch of documents with subjects ranging from politics, economics, computer science and the arts. The documents belonging to each subject are separated into the appropriate directories for each subject (you have a labeled dataset). Now, you received a new document whose subject you do not know. In which directory should it be stored? A classifier can answer this question from the documents that are already labeled.
2) Now, you received a query regarding computer science. For instance, you received the query "Good methods for finding textual similarity". Which document in the directory of computer science can provide the best response to that query? TF-IDF would be a good approach to figure that out.
So, when you are classifying documents, you are trying to make a decision about whether a document is a member of a particular class (like, say, 'about birds' or 'not about birds').
Classifiers predict the value of the class given a set of features. A good set of features will be highly discriminative - they will tell you a lot about whether the document is of one class or another.
Tf-idf (term frequency inverse document frequency) is a particular feature that seems to be discriminative for document classification tasks. There are others, like word counts (tf or term frequency) or whether a regexp matches the text or what have you.
Feature selection is the task of selecting good (discriminative) features. Tfidf is probably a good feature to select.
Using a classsication algorythm (for example naive bayes or SVM), and StringToWordVector,
would it be possible to use TF/IDF and to count terms frequency in the whole current class instead of just looking in a single document?
Let me explain, I would like the computation to give high score to words that are very frequent for a given class (not just for a given document) but not very frequent in the whole corpus.
Is it possible out of the box or does this need some extra developments?
Thanks :)
I would like the computation to give high score to words that are very frequent for a given class (not just for a given document) but not very frequent in the whole corpus.
You seem to want supervised term weighting. I'm not aware of any off-the-shelf implementation of that, but there's a host of literature about it. E.g. the weighting scheme tf-χ² replaces idf with the result of a χ² independence test, so terms that statistically depend on certain classes get boosted, and there are several others.
Tf-idf itself is by its very nature unsupervised.
I think you're confusing yourself here---what you're asking for is essentially the feature weight on that term for documents of that class. This is what the learning algorithm is intended to optimise. Just worry about a useful representation of documents, which must necessarily be invariant to the class to which they belong (since you won't know what the class is for unseen test documents).
A changed idf may help you in some scene.
You can use the idf defined as:
log(1+p(term in this class)/p(term in other class))
Disadvantages : Each class has a different idf, this can be interpreted as every term in different class has various contribution in distinguishing the category.
Application : By add the idf in Native Bayes, I get a improve in query keyword classification. And it perform good when extracting keywords.