This paper describes a deterministic method for generating natural language suited to being part of a machine translation system with meaning representations as the level for language transfer. Starting from Davidsonian/Penman meaning representations, syntactic trees are built following the Penn Parsed Corpus of Modern British English, from which the yield (i.e., the words) can be taken. The no...

Currently, the most effective complete SAT solvers are based on the DPLL algorithm augmented by clause learning. These solvers can handle many real-world problems from application areas like verification, diagnosis, planning, and design. Without clause learning, however, DPLL loses most of its effectiveness on real world problems. Recently there has been some work on obtaining a deeper understa...

Fill-in-the-blank questions are one of the main assessment devices in REAP.PT tutoring system. The problem of automatically generating the stems, i.e. the sentences that serve as basis to this type of question, has been studied mostly for English, and it remains a challenge for a language as morphologically rich as European Portuguese (EP), for which additional data scarcity problems arise. To ...

This paper describes and evaluates DQGen, which automatically generates multiple choice cloze questions to test a child’s comprehension while reading a given text. Unlike previous methods, it generates different types of distracters designed to diagnose different types of comprehension failure, and tests comprehension not only of an individual sentence but of the context that precedes it. We ev...

Conjunctive Normal Form (CNF) Boolean formulas generated from resolution or solution enumeration often have much redundancy. It is desirable to have an efficient algorithm to simplify and compact such CNF formulas on the fly. Given a clause in a CNF formula, if a subset of its literals constitutes another clause in the formula, then the first clause is said to be subsumed by the second clause. ...