Mining Entity Synonyms with Efficient Neural Set Generation

Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio

Automatic Synonym Discovery with Knowledge Bases

Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework

The instrument development to measure the verbal ability of prospective high school students

An alternative for determining an accurate major for prospective high school students is not only based on academic scores but also on the results of the scholastic aptitude test (SAT). Verbal ability is an SAT subtest that assesses language management, vocabulary, and problem-solving abilities through a complete language study. This study developed a verbal ability test instrument for junior high school students consisting of the ability of synonyms, antonyms, and analogies. The data was collected from 300 junior high school students in grade nine who took a test with dichotomous data. The data analysis approach used one-order confirmatory factor analysis (CFA) with correlation factors. The results showed that CFA with correlation factors indicated the construct validity of the instrument was valid with the index criteria value =446.80, df=389, p-value=0.02267, root mean squared error of approximation (RMSEA)=0.022, goodness of fit index (GFI)=0.91, adjusted goodness-of-fit (AGFI)=0.89, and comparative fit index (CFI)=0.98. Then, construct reliability has good reliability with coefficient values for each dimension of 0.93, 0.95, and 0.84. As for the composite reliability of 0.88. It shows that using the verbal ability test instrument is feasible and has a reliable scale to measure the ability of junior high school students

EFFECTS OF AN iPAD iBOOK ON READING COMPREHENSION, ELECTRODERMAL ACTIVITY, AND ENGAGEMENT FOR ADOLESCENTS WITH DISABILITIES

The purpose of this study was to investigate the effects of an iPad iBook for adolescents with disabilities. With its release in 2012, the iBooks Author software for the Apple iPad allows classroom teachers to create accessible and engaging textbooks. Leveraging media and interactive widgets, iBooks Author holds promise for delivering content to learners of all needs. However, little empirical research currently supports the iPad as a textbook. In this intervention study, 22 middle school students with disabilities learned to identify and understand features of textbooks. Participants were randomly assigned to one of two cohorts and alternated reading between a traditional textbook and iPad iBook across six science textbook chapters. Using a repeated measures design, quantitative and qualitative data were collected for reading comprehension scores, electrodermal activity, cognitive workload, and participant satisfaction. Results indicated no significant differences in reading comprehension scores, electrodermal activity levels, or cognitive workload scores. Satisfaction measures indicated students significantly preferred the iPad iBook. Emergent themes from participant interviews, fidelity checks, and task analyses are also discussed