Learning by reading is one of the m

Learning by reading is one of the most challenging tasks in Artificial Intelligence. It subsumes at least two subtasks that are open problem in AI. The first subtask - natural language understanding - is difficult because of the ambiguity of language, which in part results from the speaker's intentional omission of information that the recipient is assumed to know. Researchers have made progress on isolated tasks in the syntactic and semantic analysis of utterances but there have been few attempts to integrate them into a comprehensive system that builds machine-sensible representations of the content of text. The second subtask - knowledge integration - involves combining new information gleaned from individual sentences of texts, along with a priori knowledge, to form a comprehensive and computationally useful knowledge base. Researchers in knowledge engineering have studied some of the core issues, such as flexible matching of information structures and integrating new concepts into ontologies, but the source of new information has been knowledge engineers, or subject-matter experts trained as knowledge engineers, who perform knowledge integration manually.

Arguably, although these subtasks are undeniably difficult, combining them might simplify both. The knowledge integration task produces a knowledge base that might help natural language understanding, and natural language understanding might automatically produce new content for knowledge integration. In fact, if the two tasks are tightly coupled in a cycle, a learning by reading system might start with only general knowledge and a corpus of relevant texts and bootstrap itself to a state of domain expertise.

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Learning by reading is one of the most challenging tasks in Artificial Intelligence. It subsumes at least two subtasks that are open problem in AI. The first subtask - natural language understanding - is difficult because of the ambiguity of language, which in part results from the speaker's intentional omission of information that the recipient is assumed to know. Researchers have made ​​progress on isolated tasks in the syntactic and semantic analysis of utterances but there have been few attempts to integrate them into a comprehensive system that builds machine-sensible representations of the content of text. The second subtask - knowledge integration - involves combining new information gleaned from individual sentences of texts, along with a priori knowledge, to form a comprehensive and computationally useful knowledge base. Researchers in knowledge engineering have studied some of the core issues, such as flexible matching of information structures and integrating new concepts into ontologies, but the source of new information has been knowledge engineers, or subject-matter experts trained as knowledge engineers, who perform knowledge. Integration manually. Arguably, although these subtasks are Undeniably Difficult, Might Simplify Combining them Both. The knowledge integration task produces a knowledge base that might help natural language understanding, and natural language understanding might automatically produce new content for knowledge integration. In fact, if the two tasks are tightly coupled in a cycle, a learning by reading system might start with only general knowledge and a corpus of relevant texts and bootstrap itself to a state of domain expertise.