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InterText develops a comprehensive framework for understanding intertextual relationships in natural language processing. By creating new datasets and models, it enhances the interpretation of texts in various contexts, facilitating better insights in fields like research and journalism.
Interpreting text in the context of other texts is very hard: it requires understanding the fine-grained semantic relationships between documents called intertextual relationships.
This is critical in many areas of human activity, including research, business, journalism, and others.
However, finding and interpreting intertextual relationships and tracing information throughout heterogeneous sources remains a tedious manual task.
Natural language processing (NLP) fails to adequately support it:…
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