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Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user's preferences, thus making the recommendation process more(More)
In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unanno-tated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples , and therefore are potentially very useful in natural language applications. However , existing(More)
This paper focuses on a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. Wolfie is part of an integrated system that learns to transform sentences into representations such as(More)
This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of words paired with meaning representations. Wolfie is part of an integrated system that learns to parse novel sentences into semantic(More)
The authors analyzed data from the 2002 National Study of the Changing Workforce (N = 3,504) to investigate relationships among availability of formal organizational family support (family benefits and alternative schedules), job autonomy, informal organizational support (work-family culture, supervisor support, and coworker support), perceived control, and(More)
In this paper, we describe the Adaptive Place Advisor, a conversational interface designed to help users decide on a destination. We view the selection of destinations as an interactive process of constraint satisfaction, with the advisory system proposing attributes and the human responding. We further characterize this task in terms of heuristic search,(More)
This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate and each training input is assumed to be accompanied by all of its legal outputs. Any other outputs generated by an incomplete program implicitly represent(More)
Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic(More)
For most natural language processing tasks, a parser that maps sentences into a semantic representation is signiicantly more useful than a grammar or au-tomata that simply recognizes syntactically well-formed strings. This paper reviews our work on using inductive logic programming methods to learn de-terministic shift-reduce parsers that translate natural(More)