Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability
Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms.
['[EMPTY]', 'main', '+“ecab”', '+“depression”', '+“xx”', '+many']
We see fairly general terms and some boilerplate language. Here it is necessary to explore the raw text to discover the contexts for these phrases. This is easily done using our package. For example, one positively marked decision has: Not every injury or illness that is related to a claimant’s employment is compensable. In the case of Lillian Cutler, the Board explained some distinctions as to the type of employment situations giving rise to a compensable emotional condition under FECA. C.E., Docket No. 10-461 (issued November 23, 2010) (emphasis added). Another is: To establish that an emotional condition arose in the performance of duty, a claimant must submit the following: (1) medical evidence establishing that she has an emotional or psychiatric disorder; (2) factual evidence identifying employment factors or incidents alleged to have caused or contributed to the condition; and (3) rationalized medical opinion evidence establishing that the emotional condition is causally related to the identified compensable employment factors. T.G., 58 ECAB 189 (2006) (emphasis added). Each column corresponds to dropping more and more terms from consideration. Note that the transition from the third to fourth column drops one of the case-references, “Lillian Cutler,” even though we did not explicitly drop those words and phrases. CCS selected phrases are picked in the context of other phrases. Because we removed “xx” (indicating a case number), “Lillian Cutler” is no longer selected, along with other phrases including parts of this phrase and “xx.” Instead we obtain a cluster of phrases showcasing different aspects of these cases. Dropping phrases can only affect a summary if those phrases are in the summary. The final two columns are the same because none of the additional phrases were in the summary from column 4. Care must be taken to understand the complex dependencies between phrases.