Question

This is a question about linear regression with ngrams, using Tf-IDF (term frequency – inverse document frequency). To do this, I am using numpy sparse matrices and sklearn for linear regression.

I have 53 cases and over 6000 features when using unigrams. The predictions are based on cross validation using LeaveOneOut.

When I create a tf-idf sparse matrix of only unigram scores, I get slightly better predictions than when I create a tf-idf sparse matrix of unigram+bigram scores. The more columns I add to the matrix (columns for trigram, quadgram, quintgrams, etc.), the less accurate the regression prediction.

Is this common? How is this possible? I would have thought that the more features, the better.

Answer 1

It’s not common for bigrams to perform worse than unigrams, but there are situations where it may happen. In particular, adding extra features may lead to overfitting. Tf-idf is unlikely to alleviate this, as longer n-grams will be rarer, leading to higher idf values.

I’m not sure what kind of variable you’re trying to predict, and I’ve never done regression on text, but here’s some comparable results from literature to get you thinking:

• In random text generation with small (but non-trivial) training sets, 7-grams tend to reconstruct the input text almost verbatim, i.e. cause complete overfit, while trigrams are more likely to generate “new” but still somewhat grammatical/recognizable text (see Jurafsky & Martin; can’t remember which chapter and I don’t have my copy handy).
• In classification-style NLP tasks performed with kernel machines, quadratic kernels tend to fare better than cubic ones because the latter often overfit on the training set. Note that unigram+bigram features can be thought of as a subset of the quadratic kernel’s feature space, and {1,2,3}-grams of that of the cubic kernel.

Exactly what is happening depends on your training set; it might simply be too small.