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IBM Unraveling Language Patterns is an algorithm for automatically extracting patterns that characterize subtle linguistic phenomena. To that end, IBM Unraveling Language Patterns augments each term of input text with multiple layers of linguistic information. These different facets of the text terms are systematically combined to reveal rich patterns.
For example, in the context of argumentation mining, each of the following sentences includes a claim for a [topic]:
Opponents often argue that the open primary is unconstitutional. [Open Primaries]
Prof. Smith suggested that affirmative action devalues the accomplishments of discriminated groups. [Affirmative Action]
The majority stated that the First Amendment does not guarantee the right to offend others. [Freedom of Speech]
These sentences share almost no words in common, however, they are similar at a more abstract level. A human observer may notice the following underlying common structure, or pattern:
[someone][argue/suggest/state][that][topic term][sentiment term].
IBM Unraveling Language Patterns aims to automatically capture such underlying structures of the given data. In the above examples, it finds the pattern
where [express] stands for all its (in)direct hyponyms, and [noun,topic] means a noun which is also related to the topic.
IBM Unraveling Language Patterns is described in the following paper: