It is generally accepted amongst CS educators that creating ''good'' variable names is important. However, it is difficult to understand what makes a variable name ''good''. This makes it challenging to both educate and assess students on this topic. This is particularly true for high school teachers, many of whom are not experienced programmers. We therefore develop a decision-tree taxonomy of variable identifiers, with the goal of better equipping teachers to explain, understand, and assess what is ''good'' when it comes to their students' variable naming conventions and behaviours. Our taxonomy is based on 152 real high school student submissions to an automated assessment system used for a New Zealand high school programming standard in the Python programming language. From this, we perform an inductive analysis of 831 variable identifiers, and create a classification scheme made up of 25 distinct categories. We demonstrate that this taxonomy is usable outside of the developers, by scoring high inter-rater reliability with someone not involved in its development, and discuss how it could be adopted by high school educators.

A major challenge in paraphrase research is the lack of parallel corpora.In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs.The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work.We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification.In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ∼70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction.We make our code and data freely available.1

Neural network techniques are widely applied to obtain high-quality distributed representations of words (i.e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from context such that they can encode both semantic and syntactic relationships between words. However, it is quite challenging to handle unseen or rare words with insufficient context. Inspired by the study on the word recognition process in cognitive psychology, in this article, we propose to take advantage of seemingly less obvious but essentially important morphological knowledge to address these challenges. In particular, we introduce a novel neural network architecture called KNET that leverages both words’ contextual information and morphological knowledge to learn word embeddings. Meanwhile, this new learning architecture is also able to benefit from noisy knowledge and balance between contextual information and morphological knowledge. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed KNET framework can greatly enhance the effectiveness of word embeddings.