1. A method comprising using at least one hardware processor for: receiving a context, wherein the context is a digital text segment; identifying evidence with respect to the context in at least one content resource, wherein the at least one content resource comprises at least one of unstructured digital text data and free-text digital corpora, and wherein the identifying comprises: a) identifying context-free features in the at least one content resource, that generally characterize certain one or more digital text segments in the at least one content resource as evidence, wherein the context-free features comprise at least one of (i) numbers and (ii) keywords that indicate a quantitative analysis of data, and wherein the identifying of the context-free features comprises applying a first machine learning classifier to the at least one content resource, wherein the first machine learning classifier is trained on digital texts in which evidence was manually labeled, and b) identifying, in the certain one or more digital text segments, context features indicative of the relevance of at least some of the certain one or more digital text segments to the context, wherein the relevance is defined as direct support of or opposition to the context, and wherein the identifying of the context features comprises applying a second machine learning classifier to the certain one or more digital text segments, wherein the second machine learning classifier is trained on digital texts that were output by the first machine learning classifier and were manually labeled as evidence that supports or opposes a training context; and outputting a list of said identified evidence.
1. A method comprising using at least one hardware processor for: receiving a context, wherein the context is a digital text segment; identifying evidence with respect to the context in at least one content resource, wherein the at least one content resource comprises at least one of unstructured digital text data and free-text digital corpora, and wherein the identifying comprises: a) identifying context-free features in the at least one content resource, that generally characterize certain one or more digital text segments in the at least one content resource as evidence, wherein the context-free features comprise at least one of (i) numbers and (ii) keywords that indicate a quantitative analysis of data, and wherein the identifying of the context-free features comprises applying a first machine learning classifier to the at least one content resource, wherein the first machine learning classifier is trained on digital texts in which evidence was manually labeled, and b) identifying, in the certain one or more digital text segments, context features indicative of the relevance of at least some of the certain one or more digital text segments to the context, wherein the relevance is defined as direct support of or opposition to the context, and wherein the identifying of the context features comprises applying a second machine learning classifier to the certain one or more digital text segments, wherein the second machine learning classifier is trained on digital texts that were output by the first machine learning classifier and were manually labeled as evidence that supports or opposes a training context; and outputting a list of said identified evidence. 3. The method of claim 1 , wherein said identifying of said evidence comprises using a classifier for each type of evidence.