Multi-Feature Fusion Based Structural Deep Neural Network for Predicting Answer Time on Stack Overflow

Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics.However,many questions are usually not answered quickly enough.Since the questioners are eager to know the specific time interval at which a question can be answered,it becomes an important task for Stack Overflow to feedback the answer time to the question.To address this issue,we propose a model for predicting the answer time of questions,named Predicting Answer Time(i.e.,PAT model),which consists of two parts:a feature acquisition and fusion model,and a deep neural network model.The framework uses a variety of features mined from questions in Stack Overflow,including the question description,question title,question tags,the creation time of the question,and other temporal features.These features are fused and fed into the deep neural network to predict the answer time of the question.As a case study,post data from Stack Overflow are used to assess the model.We use traditional regression algorithms as the baselines,such as Linear Regression,K-Nearest Neighbors Regression,Support Vector Regression,Multilayer Perceptron Regression,and Random Forest Regression.Experimental results show that the PAT model can predict the answer time of questions more accurately than traditional regression algorithms,and shorten the error of the predicted answer time by nearly 10 hours.

Interpretation of English Ambiguous VerbLocative Prepositional Phrase Constructions by Mandarin and Spanish Speakers:Evidence for the Representational Deficit Hypothesis

This paper presents an empirical study of the acquisition of English ambiguous verb-locative prepositional phrase constructions （VLPPs） by adult Mandarin and Spanish speakers. This study assumes that the semantic properties of the target VLPPs that relate to change-of-location in sentences such as The boat floated under the bridge arise from an uninterpretable syntactic feature selected by English but unselected by Mandarin Chinese and Spanish. Results obtained from an animated cartoon selection task indicate that neither the Mandarin nor the Spanish speakers at any level of English proficiency possess native-like interpretative knowledge. Tense/ Aspect effects on the interpretation of the target constructions by Spanish speakers were also found. These results are interpreted as consistent with the Representational Deficit Hypothesis view （Hawkins, 2003, 2005） of adult second language acquisition.