Polynomial-Time Identification of Multiple Context-Free Languages from Positive Data and Membership Queries

This paper presents an efficient algorithm that identifies a rich subclass of multiple context-free languages in the limit from positive data and membership queries by observing where each tuple of strings may occur in sentences of the language of the learning target. Our technique is based on Clark et al.'s work (ICGI 2008) on learning of a subclass of context-free languages. Our algorithm learns those context-free languages as well as many non-context-free languages.

• Type: Conference
• Year: 2010
• Where: ICGI
• Authors: Ryo Yoshinaka
• Keywords: Context-free Languages, ICGI 2010, Multiple Context-free Languages, Natural Language Processing, Rich Subclass