OpenRaiser/Intern-Atlas huggingface dataset:
Cross-lingual Text Classification via Model Translation with Limited Dictionaries
Authors: Ruochen Xu, Yiming Yang, Hanxiao Liu, Andrew Hsi
Venue: CIKM
Year: 2016

Cross-lingual text classification (CLTC) refers to the task of classifying documents in different languages into the same taxonomy of categories. An open challenge in CLTC is to classify documents for the languages where labeled training data are not available. Existing approaches rely on the availability of either high-quality machine translation of documents (to the languages where massively training data are available), or rich bilingual dictionaries for effective translation of trained classification models (to the languages where labeled training data are lacking). This paper studies the CLTC challenge under the assumption that neither condition is met. That is, we focus on the problem of translating classification models with highly incomplete bilingual dictionaries. Specifically, we propose two new approaches that combines unsupervised word embedding in different languages, supervised mapping of embedded words across languages, and probabilistic translation of classification models. The approaches show significant performance improvement in CLTC on a benchmark corpus of Reuters news stories (RCV1/RCV2) in English, Spanish, German, French and Chinese and an internal dataset in Uzbek, compared to representative baseline methods using conventional bilingual dictionaries or highly incomplete ones.
"Shall I Be Your Chat Companion?": Towards an Online Human-Computer Conversation System
Authors: Rui Yan, Yiping Song, Xiangyang Zhou, Hua Wu
Venue: CIKM
Year: 2016

To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques in information retrieval, natural language processing, and data management, etc. The challenges lie in how to respond like a human, and to maintain a relevant, meaningful, and continuous conversation. The arrival of big data era reveals the feasibility to create such a system empowered by data-driven approaches. We can now organize the conversational data as a chat companion. In this paper, we introduce a chat companion system, which is a practical conversation system between human and computer as a real application. Given the human utterances as queries, our proposed system will respond with corresponding replies retrieved and highly ranked from a massive conversational data repository. Note that 'practical' here indicates effectiveness and efficiency: both issues are important for a real-time system based on a massive data repository. We have two scenarios of single-turn and multi-turn conversations. In our system, we have a base ranking without conversational context information (for single-turn) and a context-aware ranking (for multi-turn). Both rankings can be conducted either by a shallow learning or deep learning paradigm. We combine these two rankings together in optimization. In the experimental setups, we investigate the performance between effectiveness and efficiency for the proposed methods, and we also compare against a series of baselines to demonstrate the advantage of the proposed framework in terms of [email protected], MAP, and nDCG. We present a new angle to launch a practical online conversation system between human and computer.
Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval
Authors: Ting-Kun Yan, Xin-Shun Xu, Shanqing Guo, Zi Huang, Xiaolin Wang
Venue: CIKM
Year: 2016

Recently, multimodal hashing techniques have received considerable attention due to their low storage cost and fast query speed for multimodal data retrieval. Many methods have been proposed; however, there are still some problems that need to be further considered. For example, some of these methods just use a similarity matrix for learning hash functions which will discard some useful information contained in original data; some of them relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error; some of them are not robust to noise. All these problems may degrade the performance of a model. To consider these problems, in this paper, we propose a novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH). Specifically, SRDMH tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning. In addition, it learns hashing functions and binary codes directly instead of relaxing the binary constraints so as to avoid large quantization error problem. Moreover, to make it robust and easy to solve, we further integrate a flexible l2,p loss with nonlinear kernel embedding and an intermediate presentation of each instance. Finally, an alternating algorithm is proposed to solve the optimization problem in SRDMH. Extensive experiments are conducted on three benchmark data sets. The results demonstrate that the proposed method (SRDMH) outperforms or is comparable to several state-of-the-art methods for cross-modal retrieval task.
APAM: Adaptive Eager-Lazy Hybrid Evaluation of Event Patterns for Low Latency
Authors: Ilyeop Yi, Jae-Gil Lee, Kyu-Young Whang
Venue: CIKM
Year: 2016

Event pattern detection refers to identifying combinations of events matched to a user-specified query event pattern from a real-time event stream. Latency is an important measure of the performance of an event pattern detection system. Existing methods can be classified into the eager evaluation method and the lazy evaluation method depending on when each event arrival is evaluated. These methods have advantages and disadvantages in terms of latency depending on the event arrival rate. In this paper, we propose a hybrid eager-lazy evaluation method that combines the advantages of both methods. For each event type, the hybrid method, which we call APAM ( Adaptive Partitioning-And-Merging), determines which method to use: eager or lazy. We also propose a formal cost model to estimate the latency and propose a method of finding the optimal partition based on the cost model. Finally, we show through experiments that our method can improve the latency by up to 361.48 times over the eager evaluation method and 27.94 times over the lazy evaluation method using a synthetic data set.
Data-Driven Contextual Valence Shifter Quantification for Multi-Theme Sentiment Analysis
Authors: Hongkun Yu, Jingbo Shang, Meichun Hsu, Malú Castellanos, Jiawei Han
Venue: CIKM
Year: 2016

Users often write reviews on different themes involving linguistic structures with complex sentiments. The sentiment polarity of a word can be different across themes. Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in. Both challenges cannot be modeled effectively and explicitly in traditional sentiment analysis. Studying both phenomena requires multi-theme sentiment analysis at the word level, which is very interesting but significantly more challenging than overall polarity classification. To simultaneously resolve the <i>multi-theme</i> and <i>sentiment shifting</i> problems, we propose a data-driven framework to enable both capabilities: (1) polarity predictions of the same word in reviews of different themes, and (2) discovery and quantification of contextual valence shifters. The framework formulates multi-theme sentiment by factorizing the review sentiments with theme/word embeddings and then derives the shifter effect learning problem as a logistic regression. The improvement of sentiment polarity classification accuracy demonstrates not only the importance of <i>multi-theme</i> and <i>sentiment shifting</i>, but also effectiveness of our framework. Human evaluations and case studies further show the success of multi-theme word sentiment predictions and automatic effect quantification of contextual valence shifters.
Inferring Traffic Incident Start Time with Loop Sensor Data
Authors: Mingxuan Yue, Liyue Fan, Cyrus Shahabi
Venue: CIKM
Year: 2016

Traffic incidents and their impacts have been largely studied to improve road safety and to reduce incurred life and economic losses. However, the inaccuracy of incident data collected from transportation agencies, especially the start time, poses a great challenge to traffic incident research. We present INFIT, a system that infers the incident start time utilizing traffic data collected by loop sensors. The core of INFIT is IIG, our newly developed inference algorithm. The key idea is that IIG considers the traffic speed at multiple upstream locations, to mitigate the randomness in traffic data and to distinguish among multiple impact factors. INFIT includes an interactive interface with real-world incident datasets. We demonstrate INFIT with three exploratory use cases and show the usefulness of our inference algorithms.
Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks
Authors: Jiangfeng Zeng, Ke Zhou, Xiao Ma, Fuhao Zou, Hua Wang
Venue: CIKM
Year: 2016

Many online social networks can be described by signed networks, where positive links signify friendships, trust and like; while negative links indicate enmity, distrust and dislike. Predicting the sign of the links in these networks has attracted a great deal of attentions in the areas of friendship recommendation and trust relationship prediction. Existing methods for sign prediction tend to rely on path-based features which are somehow limited to the sparsity problem of the network. In order to solve this issue, in this paper, we introduce a novel sign prediction model by exploiting cluster-based meta paths, which can take advantage of both local and global information of the input networks. First, cluster-based meta paths based features are constructed by incorporating the newly generated clusters through hierarchically clustering the input networks. Then, the logistic regression classifier is employed to train the model and predict the hidden signs of the links. Extensive experiments on Epinions and Slashdot datasets demonstrate the efficiency of our proposed method in terms of Accuracy and Coverage.
Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
Authors: Baichuan Zhang, Murat Dundar, Mohammad Al Hasan
Venue: CIKM
Year: 2016

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal x Normal x Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.
Retweet Prediction with Attention-based Deep Neural Network
Authors: Qi Zhang, Yeyun Gong, Jindou Wu, Haoran Huang, Xuanjing Huang
Venue: CIKM
Year: 2016

On Twitter-like social media sites, the re-posting statuses or tweets of other users are usually considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention in recent years. Previous methods studied the problem using various linguistic features, personal information of users, and many other manually constructed features to achieve the task. Usually, feature engineering is a laborious task, we require to obtain the external sources and they are difficult or not always available. Recently, deep learning methods have been used in the industry and research community for their ability to learn optimal features automatically and in many tasks, deep learning methods can achieve state-of-the art performance, such as natural language processing, computer vision, image classification and so on. In this work, we proposed a novel attention-based deep neural network to incorporate contextual and social information for this task. We used embeddings to represent the user, the user's attention interests, the author and tweet respectively. To train and evaluate the proposed methods, we also constructed a large dataset collected from Twitter. Experimental results showed that the proposed method could achieve better results than the previous state-of-the-art methods.
Who are My Familiar Strangers?: Revealing Hidden Friend Relations and Common Interests from Smart Card Data
Authors: Fusang Zhang, Beihong Jin, Tingjian Ge, Qiang Ji, Yanling Cui
Venue: CIKM
Year: 2016

The newly emerging location-based social networks (LBSN) such as Tinder and Momo extends social interaction from friends to strangers, providing novel experiences of making new friends. Familiar strangers refer to the strangers who meet frequently in daily life and may share common interests; thus they may be good candidates for friend recommendation. In this paper, we study the problem of discovering familiar strangers, specifically, public transportation trip companions, and their common interests. We collect 5.7 million transaction records of smart cards from about 3.02 million people in the city of Beijing, China. We first analyze this dataset and reveal the temporal and spatial characteristics of passenger encounter behaviors. Then we propose a stability metric to measure hidden friend relations. This metric facilitates us to employ community detection techniques to capture the communities of trip companions. Further, we infer common interests of each community using a topic model, i.e., LDA4HFC (Latent Dirichlet Allocation for Hidden Friend Communities) model. Such topics for communities help to understand how hidden friend clusters are formed. We evaluate our method using large-scale and real-world datasets, consisting of two-week smart card records and 901,855 Point of Interests (POIs) in Beijing. The results show that our method outperforms three baseline methods with higher recommendation accuracy. Moreover, our case study demonstrates that the discovered topics interpret the communities very well.
Scalable Local-Recoding Anonymization using Locality Sensitive Hashing for Big Data Privacy Preservation
Authors: Xuyun Zhang, Christopher Leckie, Wanchun Dou, Jinjun Chen, Kotagiri Ramamohanarao, Zoran Salcic
Venue: CIKM
Year: 2016

While cloud computing has become an attractive platform for supporting data intensive applications, a major obstacle to the adoption of cloud computing in sectors such as health and defense is the privacy risk associated with releasing datasets to third-parties in the cloud for analysis. A widely-adopted technique for data privacy preservation is to anonymize data via local recoding. However, most existing local-recoding techniques are either serial or distributed without directly optimizing scalability, thus rendering them unsuitable for big data applications. In this paper, we propose a highly scalable approach to local-recoding anonymization in cloud computing, based on Locality Sensitive Hashing (LSH). Specifically, a novel semantic distance metric is presented for use with LSH to measure the similarity between two data records. Then, LSH with the MinHash function family can be employed to divide datasets into multiple partitions for use with MapReduce to parallelize computation while preserving similarity. By using our efficient LSH-based scheme, we can anonymize each partition through the use of a recursive agglomerative $k$-member clustering algorithm. Extensive experiments on real-life datasets show that our approach significantly improves the scalability and time-efficiency of local-recoding anonymization by orders of magnitude over existing approaches.
Influence-Aware Truth Discovery
Authors: Hengtong Zhang, Qi Li, Fenglong Ma, Houping Xiao, Yaliang Li, Jing Gao, Lu Su
Venue: CIKM
Year: 2016

In the age of big data, information for the same entity can be obtained from different sources, which is inevitably conflicting. Therefore, aggregation methods are needed to identify the trustworthy information from such conflicting data. Truth discovery, which improves the aggregation results by estimating source trustworthiness and discovering truths simultaneously, has become an emerging field. Most truth discovery methods assume that sources make their claims independently, which may not be true in practice. As a matter of fact, influences among sources are ubiquitous and the claims made by one source may be influenced by others. Although there is some work that considers source correlation, those methods are designed to handle categorical claims, which is not general enough to represent the complicated real world applications. To tackle these challenges in truth discovery, we propose an unsupervised probabilistic model named IATD. The model takes source correlations as prior for influence derivation. To model influences among sources, we introduce "claim trustworthiness", which fuses the trustworthiness of the source which provides the claim and the trustworthiness of its influencers. Besides, the proposed model can handle different data types using different distributions in the probabilistic model. Experiments on real-world datasets show that IATD model can improve the aggregation performance compared with the state-of-the-art truth discovery approaches. The properties of IATD model are further illustrated using simulated datasets.
A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge
Authors: Yuhao Zhang, Wenji Mao, Daniel Dajun Zeng
Venue: CIKM
Year: 2016

Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short texts often restrict the performance of traditional topic models like LDA. Recently proposed Biterm Topic Model (BTM) which models word co-occurrence patterns directly, is revealed effective for topic detection in short texts. However, BTM has two main drawbacks. It needs to manually specify topic number, which is difficult to accurately determine when facing new corpora. Besides, BTM assumes that two words in same term should belong to the same topic, which is often too strong as it does not differentiate two types of words (i.e. general words and topical words). To tackle these problems, in this paper, we propose a non-parametric topic model npCTM with the above distinction. Our model incorporates the Chinese restaurant process (CRP) into the BTM model to determine topic number automatically. Our model also distinguishes general words from topical words by jointly considering the distribution of these two word types for each word as well as word coherence information as prior knowledge. We carry out experimental studies on real-world twitter dataset. The results demonstrate the effectiveness of our method to discover coherent topics compared with the baseline methods.
Derivative Delay Embedding: Online Modeling of Streaming Time Series
Authors: Zhifei Zhang, Yang Song, Wei Wang, Hairong Qi
Venue: CIKM
Year: 2016

The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution. For time series modeling, most existing works make some unrealistic assumptions such as the input data is of fixed length or well aligned, which requires extra effort on segmentation or normalization of the raw streaming data. Although some literature claim their approaches to be invariant to data length and misalignment, they are too time-consuming to model a streaming time series in an online manner. We propose a novel and more practical online modeling and classification scheme, DDE-MGM, which does not make any assumptions on the time series while maintaining high efficiency and state-of-the-art performance. The derivative delay embedding (DDE) is developed to incrementally transform time series to the embedding space, where the intrinsic characteristics of data is preserved as recursive patterns regardless of the stream length and misalignment. Then, a non-parametric Markov geographic model (MGM) is proposed to both model and classify the pattern in an online manner. Experimental results demonstrate the effectiveness and superior classification accuracy of the proposed DDE-MGM in an online setting as compared to the state-of-the-art.
Clustering Speed in Multi-lane Traffic Networks
Authors: Bing Zhang, Goce Trajcevski, Feiying Liu
Venue: CIKM
Year: 2016

We address the problem of efficient spatio-temporal clustering of speed data in road segments with multiple lanes. We postulate that the navigation/route plans typically reported by different providers as a single-value need not be accurate in multi-lane networks. Our methodology generates lane-aware distribution of speed from GPS data and agglomerates the basic space and time units into larger clusters. Thus, we achieve a compact description of speed variations which can be subsequently used for more accurate trips planning. We provide experiments that demonstrate the benefits of our proposed approaches.
Efficient Orthogonal Non-negative Matrix Factorization over Stiefel Manifold
Authors: Wei Emma Zhang, Mingkui Tan, Quan Z. Sheng, Lina Yao, Qinfeng Shi
Venue: CIKM
Year: 2016

Orthogonal Non-negative Matrix Factorization (ONMF) ap- proximates a data matrix X by the product of two lower- dimensional factor matrices: X ≈ UVT, with one of them orthogonal. ONMF has been widely applied for clustering, but it often suffers from high computational cost due to the orthogonality constraint. In this paper, we propose a method, called Nonlinear Riemannian Conjugate Gradient ONMF (NRCG-ONMF), which updates U and V alterna- tively and preserves the orthogonality of U while achiev- ing fast convergence speed. Specifically, in order to update U, we develop a Nonlinear Riemannian Conjugate Gradi- ent (NRCG) method on the Stiefel manifold using Barzilai- Borwein (BB) step size. For updating V, we use a closed- form solution under non-negativity constraint. Extensive experiments on both synthetic and real-world data sets show consistent superiority of our method over other approaches in terms of orthogonality preservation, convergence speed and clustering performance.