As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

We study tag recommendation for questions in community question answering (CQA). Tags represent the semantic summarization of questions are useful for navigation and expert finding in CQA and can facilitate content consumption such as searching and mining in these web sites. The task is challenging, as both questions and tags are short and a large fraction of tags are tail tags which occur very infrequently. To solve these problems, we propose matching questions and tags not only by themselves, but also by similar questions and similar tags. The idea is then formalized as a model in which we calculate question-tag similarity using a linear combination of similarity with similar questions and tags weighted by tag importance.Question similarity, tag similarity, and tag importance are learned in a supervised random walk framework by fusing multiple features. Our model thus can not only accurately identify question-tag similarity for head tags, but also improve the accuracy of recommendation of tail tags. Experimental results show that the proposed method significantly outperforms state-of-the-art methods on tag recommendation for questions. Particularly, it improves tail tag recommendation accuracy by a large margin.

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification.Most existing models rely on a single distributed representation for each word.This is problematic for sentiment classification because words are often polysemous and each word can contain different sentiment polarities under different topics.We address this issue by learning topic-enriched multi-prototype word embeddings (TMWE).In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word.Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model.

We propose a semi-supervised learning method for improving why-question answering (why-QA). The key of our method is to generate training data (question-answer pairs) from causal relations in texts such as "[Tsunamis are generated]( effect ) because [the ocean's water mass is displaced by an earthquake]( cause )." A naive method for the generation would be to make a question-answer pair by simply converting the effect part of the causal relations into a why-question, like "Why are tsunamis generated?" from the above example, and using the source text of the causal relations as an answer. However, in our preliminary experiments, this naive method actually failed to improve the why-QA performance. The main reason was that the machine-generated questions were often incomprehensible like "Why does (it) happen?", and that the system suffered from overfitting to the results of our automatic causality recognizer. Hence, we developed a novel method that effectively filters out incomprehensible questions and retrieves from texts answers that are likely to be paraphrases of a given causal relation. Through a series of experiments, we showed that our approach significantly improved the precision of the top answer by 8% over the current state-of-the-art system for Japanese why-QA.

Chinese chunking has traditionally been solved by assuming gold standard word segmentation.We find that the accuracies drop drastically when automatic segmentation is used.Inspired by the fact that chunking knowledge can potentially improve segmentation, we explore a joint model that performs segmentation, POS-tagging and chunking simultaneously.In addition, to address the sparsity of full chunk features, we employ a semi-supervised method to derive chunk cluster features from large-scale automatically-chunked data.Results show the effectiveness of the joint model with semi-supervised features.

Recent years have witnessed the boom of online sharing media contents, which raise significant challenges in effective management and retrieval. Though a large amount of efforts have been made, precise retrieval on video shots with certain topics has been largely ignored. At the same time, due to the popularity of novel time-sync comments, or so-called "bullet-screen comments", video semantics could be now combined with timestamps to support further research on temporal video labeling. In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. To be specific, due to the informal expression of bullet-screen comments, we first propose a temporal deep structured semantic model (T-DSSM) to represent comments into semantic vectors by taking advantage of their temporal correlation. Then, video highlights are recognized and labeled via semantic vectors in a supervised way. Extensive experiments on a real-world dataset prove that our framework could effectively label video highlights with a significant margin compared with baselines, which clearly validates the potential of our framework on video understanding, as well as bullet-screen comments interpretation.

Global information such as event-event association, and latent local information such as fine-grained entity types, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-of-speech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as event-event association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.

Fake news spreading in social media severely jeopardizes the veracity of online content. Fortunately, with the interactive and open features of microblogs, skeptical and opposing voices against fake news always arise along with it. The conflicting information, ignored by existing studies, is crucial for news verification. In this paper, we take advantage of this "wisdom of crowds" information to improve news verification by mining conflicting viewpoints in microblogs. First, we discover conflicting viewpoints in news tweets with a topic model method. Based on identified tweets' viewpoints, we then build a credibility propagation network of tweets linked with supporting or opposing relations. Finally, with iterative deduction, the credibility propagation on the network generates the final evaluation result for news. Experiments conducted on a real-world data set show that the news verification performance of our approach significantly outperforms those of the baseline approaches.

Reordering poses a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present a novel reordering approach utilizing sparse features based on dependency word pairs. Each instance of these features captures whether two words, which are related by a dependency link in the source sentence dependency parse tree, follow the same order or are swapped in the translation output. Experiments on Chinese-to-English translation show a statistically significant improvement of 1.21 BLEU point using our approach, compared to a state-of-the-art statistical MT system that incorporates prior reordering approaches.