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Extracting Word Relationships from Unstructured Data (Learning Human Activities from General Websites)

One of the biggest challenges of instructing robots in natural language, is the conversion of goals into executable instructions [1]. A core problem in this area is to train the robot to identify the specific actions (verbs) that help fulfil the goal using the objects (nouns) at its disposal. This project presents a new algorithm, which we call “Extracto”, to extract noun-verb-noun relationships from unstructured text. Extracto is built on the Coupled Pattern Learner framework [2,3] presented by the creators of the ‘Never Ending Language Learner’ (NELL) project. Extracto gives a 70% precision in identifying suitable action verbs, given two nouns. We have extended this to create the “ExtractoCategorize” algorithm that extracts actions for the categories of the respective nouns instead of the nouns themselves. This increases extraction precision to 82%. Since this is a first of its kind method created purely for this purpose (noun-verb-noun extraction), we have benchmarked it against related algorithms that extract semantic relationships from unstructured text. Existing algorithms reach precision levels of 70 91% [4,5,6,7], which is in line with Extracto’s performance.