Teaching Machines to Extract World Knowledge from Text

A point made ad nauseam in the literature is that general-purpose AI systems will need to be able to utilize some form of world knowledge to comprehend the situations that they face. While raw text has been proposed as a potentially-useful source of such knowledge (see https://www.researchgate.net/publication/215991030_A_First_Experimental_Demonstration_of_Massive_Knowledge_Infusion ), the completely autonomous choice of learning material risks derailing the learning process towards sifting through the massive haystack of irrelevant text found, for example, on the Web, while searching for the proverbial needle of useful training material. The project seeks to develop curriculum learning techniques for directing the learning process. Depending on the internship type, the project can be adapted towards empirically demonstrating the effectiveness of curriculum learning over completely autonomous learning, towards the investigation of whether knowledge for a particular domain of interest is, even in principle, learnable from Web text, or towards the identification of appropriate natural language processing techniques for parsing text and extracting knowledge. 

curriculum learning OR natural language processing OR text mining OR knowledge extraction 

The main objective of the internship is to The project seeks to develop curriculum learning techniques for directing the learning process. Depending on the background of the intern, the project can be adapted towards empirically demonstrating the effectiveness of curriculum learning over completely autonomous learning, towards the investigation of whether knowledge for a particular domain of interest is, even in principle, learnable from Web text, or towards the identification of appropriate natural language processing techniques for parsing text and extracting knowledge.