Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability
Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR.
['method', 'time (avg sec)', 'list length (avg)', 'word freq (avg)', 'word freq (median)']
For each labeling and each method, we calculated different statistics on the word lists. We also timed the total time to generate a list. A few general trends are evident. We were quite surprised by the time statistics being insensitive to number of terms for the glmnet package. Here average runtimes for the Lasso increased modestly by about 16% to a mean of 101 seconds. We now see a substantially increased time from moving to unigrams to trigrams for the Lasso and the timing of the textreg methods exploded. In investigating this time differential further, we found many selected phrases had 5 or more words. Furthermore, most runs reached the maximum number of iterations before convergence, indicating flat surfaces. This is a weakness of greedy coordinate descent, which we discuss further below. Overall we potentially have, due to the use of boilerplate language in legal writing, long yet informative phrases that we wish to see in our summaries. This could make pre-generation of candidate phrases prohibitively expensive. The MNIR method selected all unigrams; each unigram apparently has enough difference in use across judges to be selected under the BIC penalization.