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.
Related
currently wanted to understand which model an approach I am incorporating for model development, I currently have a TF-IDF NLP model that reads in paragraphs for a document and makes a prediction based upon how many paragraphs scored a 1 label with that paragraph.
I am not sure if that is correct form of logic, should I just go with an document level model? what are the benefits and trade-offs of predicting at a paragraph level and rolling it up into a total prediction for the document vs just classifying the document itself.
Any Thoughts?
Thanks!
Depends on what problem you are trying to solve and the nature of your data.
If in one document different parts can be classified differently, it's better to make a prediction by paragraphs or even sentences. For example - quite often, the customer can be happy with one part of the product/item (the first sentence is positive). And be dissatisfied with another part of the product/item (the second sentence has a negative sentiment).
Or, if the document is entirely related to a specific topic, you can make a prediction using the entire text.
In the end, these are just assumptions. Hold out a test subset and validate your model for both cases.
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 have trained Gensim's WordToVec on a text corpus,converted it to DocToVec and then used cosine similarity to find the similarity between documents. I need to suggest similar documents. Now suppose among the top 5 suggestions for a particular document, we manually find that 3 of them are not similar.Can this feedback be incorporated in retraining the model?
It's not quite clear what you mean by "converted [a Word2Vec model] to DocToVec". The gensim Doc2Vec class doesn't use or require a Word2Vec model as input.
But, if you have many sets of hand-curated "this is a good suggestion" or "this is a bad suggestion" pairs for your corpus, you can use the model's scoring against all those to compare models, and train many variant models (with different model parameter values like size, window, min_count, sample, etc), picking the one that scores best on your tests.
That sort of automated-parameter-search is the most straightforward way to use performance on real evaluation data to adjust an unsupervised model like Word2Vec.
(Depending on the specifics of your data and problem-domain, you might also start to notice patterns in where the model is better or worse, that help you hand-tune parts of the data preprocessing. For example, a different handling of capitalization or tokenization might be suggested by error cases.)
I'm classifying content based on LDA into generic topics such as Music, Technology, Arts, Science
This is the process i'm using,
9 topics -> Music, Technology, Arts, Science etc etc.
9 documents -> Music.txt, Technology.txt, Arts.txt, Science.txt etc etc.
I've filled in each document(.txt file) with about 10,000 lines of content of what i think is "pure" categorical content
I then classify a test document, to see how well the classifier is trained
My Question is,
a.) Is this an efficient way to classify text (using the above steps)?
b.) Where should i be looking for "pure" topical content to fill each of these files? Sources which are not too large (text data > 1GB)
classification is only on "generic" topics such as the above
a) The method you describe sounds fine, but everything will depend on the implementation of labeled LDA that you're using. One of the best implementations I know is the Stanford Topic Modeling Toolbox. It is not actively developed anymore, but it worked great when I used it.
b) You can look for topical content on DBPedia, which has a structured ontology of topics/entities, and links to Wikipedia articles on those topics/entities.
I suggest you to use bag-of-words (bow) for each class you are using. Or vectors where each column is the frequency of important keywords related to the class you want to target.
Regarding the dictionaries you have DBPedia as yves referred or WordNet.
a.)The simplest solution is surely the k-nearest neighbors algorithm (knn). In fact, it will classify new texts with categorical content using an overlap metric.
You could find ressources here: https://github.com/search?utf8=✓&q=knn+text&type=Repositories&ref=searchresults
Dataset issue:
If you are dealing with classifying live user feeds, then I guess no single dataset will suffice your requirement.
Because if new movie X released, it might not catch by your classification dataset as the training dataset is obsoleted for it now.
For classification I guess to stay updated with latest datasets, use twitter training datasets. Develop dynamic algorithm which update the classifier with latest updated tweet datasets. You could select top 15-20 hash tag for each category of your choice to get most relevant dataset for each category.
Classifier:
Most of the classifier uses bag of words model, you can try out various classifiers and see which gives best result. see :
http://www.nltk.org/howto/classify.html
http://scikit-learn.org/stable/supervised_learning.html
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.