Acquiring Word-Meaning Mappings for Natural Language Interfaces

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 logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated 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, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

A Classroom Preferences Questionnaire Based on the Theory of Multiple Intelligences

A questionnaire based on Gardner\u27s Theory of Multiple Intelligences was developed and administered to forty-five second grade students in three different classrooms. Sections of the fifty-eight item questionnaire dealt with students\u27 preferences for certain classroom activities, methods of learning, and modes of social interaction. Each student\u27s responses were summarized to create an individual profile, indicating preference for linguistic, mathematical, and/ or spatial activity. In addition, students\u27 preferences for receptive and/ or expressive methods of learning, and for working by themselves, with peers, or with adults were profiled

Examining affective structure in chickens: valence, intensity, persistence and generalization measured using a conditioned place preference test

When measuring animals’ valenced behavioural responses to stimuli, the Conditioned Place Preference (CPP) test goes a step further than many approach-based and avoidance-based tests by establishing whether a learned preference for, or aversion to, the location in which the stimulus was encountered can be generated. We designed a novel, four-chambered CPP test to extend the capability of the usual CPP paradigm to provide information on four key features of animals’ affective responses: valence, scale, persistence and generalization. Using this test, we investigated the affective responses of domestic chickens (Gallus gallus domesticus) to four potentially aversive stimuli: 1. Puffs of air; 2. Sight of (robotic) snake; 3. Sprays of water; 4. Sound of conspecific alarm calls. We found conditioned avoidance of locations associated with the air puffs and water sprays (Friedman’s χ2(3) = 13.323 p > .005; χ2(3) = 14.235 p > .005), but not with the snake and alarm calls. The scale of the learned avoidance was similar for the air puff and water spray stimuli, but persistence and generalization differed. We conclude that the four chambered CPP test can have a valuable role to play in making multi-feature measurements of stimulus-generated affective responses, and we highlight the value of such measurements for improving our understanding of the structure of affect in chickens and other animals

Children's comprehension of distributive universal quantification

Our study explores why children are prone to assign a wider range of interpretations to sentences with distributive universal quantifiers each and everythan adults. Musolino (2009) proposed that children are more permissive than adults because they are prone to assign quantifier spreading interpretations to universally quantified sentences. Our results support the alternative hypothesis that children are more permissive because they are prone to assign cumulative interpretations to universally quantified sentences in a wider range of contexts than adults. Our results reveal that both children and adults assign cumulative interpretations to sentences with universally quantified objects (Three cowboys are pulling every horse), but children also tend to assign cumulative interpretations to sentences with universally quantified subjects (Every cowboy is pulling two horses). We show that children perform similarly with sentences with universally quantified NPs and sentences with numerical NPs (Three cowboys are pulling two horses). We argue that children are more permissive than adults because they are less likely than adults to perceive singular subject-verb agreement as a cue to distributive interpretation. We present a formal semantic model to explain our findings and discuss the implications of our model for recent acquisition research. (C) 2017 Elsevier B.V. All rights reserved