How to include words as numerical feature in class

Whats the best method to use the words itself as the features in any machine learning algorithm ?

The problem I have to extract word related feature from a particular paragraph. Should I use the index in the dictionary as the numerical feature ? If so, how will I normalize these ?

In general, How are words itself used as features in NLP ?

回答1:

There are several conventional techniques by which words are mapped to features (columns in a 2D data matrix in which the rows are the individual data vectors) for input to machine learning models.classification:

a Boolean field which encodes the presence or absence of that word in a given document;
a frequency histogram of a predetermined set of words, often the X most commonly occurring words from among all documents comprising the training data (more about this one in the last paragraph of this Answer);
the juxtaposition of two or more words (e.g., 'alternative' and 'lifestyle' in consecutive order have a meaning not related either component word); this juxtaposition can either be captured in the data model itself, eg, a boolean feature that represents the presence or absence of two particular words directly adjacent to one another in a document, or this relationship can be exploited in the ML technique, as a naive Bayesian classifier would do in this instanceemphasized text;
words as raw data to extract latent features, eg, LSA or Latent Semantic Analysis (also sometimes called LSI for Latent Semantic Indexing). LSA is a matrix decomposition-based technique which derives latent variables from the text not apparent from the words of the text itself.

A common reference data set in machine learning is comprised of frequencies of 50 or so of the most common words, aka "stop words" (e.g., a, an, of, and, the, there, if) for published works of Shakespeare, London, Austen, and Milton. A basic multi-layer perceptron with a single hidden layer can separate this data set with 100% accuracy. This data set and variations on it are widely available in ML Data Repositories and academic papers presenting classification results are likewise common.

回答2:

Standard approach is the "bag-of-words" representation where you have one feature per word, giving "1" if the word occurs in the document and "0" if it doesn't occur.

This gives lots of features, but if you have a simple learner like Naive Bayes, that's still OK.

"Index in the dictionary" is a useless feature, I wouldn't use it.

回答3:

tf-idf is a pretty standard way of turning words into numeric features.

You need to remember to use a learning algorithm that supports numeric featuers, like SVM. Naive Bayes doesn't support numeric features.