For content recommendation systems on social media platforms, timely, efficient, and accurate estimation of user preferences can effectively improve their performance and enhance the platforms’ activeness. However, efficiency and accuracy are often a pair of trade-offs; Accurate user preference estimation often requires tracking dynamic preference shifting with complex sequential modelling, whereas efficient systems may fail to follow the shift because of a lack of modelling capacity. This paper presents Asynchronous and Asymmetric User Preference Updating System AAUPU, a distributed collaborative filtering system, that can track hundreds of millions of users’ preferences in real time by processing streaming data. The AAUPU system finds a good balance between efficiency and accuracy, making it well-suited for large-scale personalization service needs on social media platforms. We implemented the system on the Google Cloud Platform and successfully tracked user preference for a population of 400 million active Twitter accounts. To evaluate the estimation quality, we conducted massive A/B tests on the two most important service surfaces of Twitter, involving more than ten million users. Our experimental results show that the recommender system based on the AAUPU system significantly improves the overall recommendation performance on the platform.

ROUGE is a widely adopted, automatic evaluation measure for text summarization.While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities.This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing.We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE.Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead.Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.

We study the use of temporal information in the form of timelines to enhance multidocument summarization.We employ a fully automated temporal processing system to generate a timeline for each input document.We derive three features from these timelines, and show that their use in supervised summarization lead to a significant 4.1% improvement in ROUGE performance over a state-of-the-art baseline.In addition, we propose TIMEMMR, a modification to Maximal Marginal Relevance that promotes temporal diversity by way of computing time span similarity, and show its utility in summarizing certain document sets.We also propose a filtering metric to discard noisy timelines generated by our automatic processes, to purify the timeline input for summarization.By selectively using timelines guided by filtering, overall summarization performance is increased by a significant 5.9%.

This paper presents DefMiner, a supervised sequence labeling system that identifies scientific terms and their accompanying definitions.DefMiner achieves 85% F 1 on a Wikipedia benchmark corpus, significantly improving the previous state-of-the-art by 8%.We exploit DefMiner to process the ACL Anthology Reference Corpus (ARC) -a large, real-world digital library of scientific articles in computational linguistics.The resulting automatically-acquired glossary represents the terminology defined over several thousand individual research articles.We highlight several interesting observations: more definitions are introduced for conference and workshop papers over the years and that multiword terms account for slightly less than half of all terms.Obtaining a list of popular defined terms in a corpus of computational linguistics papers, we find that concepts can often be categorized into one of three categories: resources, methodologies and evaluation metrics.

In this paper we classify the temporal relations between pairs of events on an article-wide basis.This is in contrast to much of the existing literature which focuses on just event pairs which are found within the same or adjacent sentences.To achieve this, we leverage on discourse analysis as we believe that it provides more useful semantic information than typical lexico-syntactic features.We propose the use of several discourse analysis frameworks, including 1) Rhetorical Structure Theory (RST), 2) PDTB-styled discourse relations, and 3) topical text segmentation.We explain how features derived from these frameworks can be effectively used with support vector machines (SVM) paired with convolution kernels.Experiments show that our proposal is effective in improving on the state-of-the-art significantly by as much as 16% in terms of F 1 , even if we only adopt less-than-perfect automatic discourse analyzers and parsers.Making use of more accurate discourse analysis can further boost gains to 35%.