OpenRaiser/Intern-Atlas huggingface dataset:
When is the Time Ripe for Natural Language Processing for Patent Passage Retrieval?
Authors: Linda Andersson, Mihai Lupu, João R. M. Palotti, Allan Hanbury, Andreas Rauber
Venue: CIKM
Year: 2016

Patent text is a mixture of legal terms and domain specific terms. In technical English text, a multi-word unit method is often deployed as a word formation strategy in order to expand the working vocabulary, i.e. introducing a new concept without the invention of an entirely new word. In this paper we explore query generation using natural language processing technologies in order to capture domain specific concepts represented as multi-word units. In this paper we examine a range of query generation methods using both linguistic and statistical information. We also propose a new method to identify domain specific terms from other more general phrases. We apply a machine learning approach using domain knowledge and corpus linguistic information in order to learn domain specific terms in relation to phrases' Termhood values. The experiments are conducted on the English part of the CLEF-IP 2013 test collection. The outcome of the experiments shows that the favoured method in terms of PRES and recall is when a language model is used and search terms are extracted with a part-of-speech tagger and a noun phrase chunker. With our proposed methods we improve each evaluation metric significantly compared to the existing state-of-the-art for the CLEP-IP 2013 test collection: for [email protected] by 26% (0.544 from 0.433), for [email protected] by 17% (0.631 from 0.540) and on document MAP by 57% (0.300 from 0.191).
Structural Clustering of Machine-Generated Mail
Authors: Noa Avigdor-Elgrabli, Mark Cwalinski, Dotan Di Castro, Iftah Gamzu, Irena Grabovitch-Zuyev, Liane Lewin-Eytan, Yoelle Maarek
Venue: CIKM
Year: 2016

Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind that mail extraction supports a variety of mail mining applications such as ad re-targeting, mail search, and mail summarization. We introduce new structural clustering methods that leverage the HTML structure that is common to messages generated by a same mass-sender script. We discuss how such structural clustering can be conducted at different levels of granularity, using either strict or flexible matching constraints, depending on the use cases.
Beyond Clustering: Sub-DAG Discovery for Categorising Documents
Authors: Ramakrishna B. Bairi, Mark James Carman, Ganesh Ramakrishnan
Venue: CIKM
Year: 2016

We study the problem of generating DAG-structured category hierarchies over a given set of documents associated with "importance" scores. Example application includes automatically generating Wikipedia disambiguation pages for a set of articles having click counts associated with them. Unlike previous works, which focus on clustering the set of documents using the category hierarchy as features, we directly pose the problem as that of finding a DAG structured generative mode that has maximum likelihood of generating the observed "importance" scores for each document where documents are modeled as the leaf nodes in the DAG structure. Desirable properties of the categories in the inferred DAG-structured hierarchy include document coverage and category relevance, each of which, we show, is naturally modeled by our generative model. We propose two different algorithms for estimating the model parameters. One by modeling the DAG as a Bayesian Network and estimating its parameters via Gibbs Sampling; and the other by estimating the path probabilities using the Expectation Maximization algorithm. We empirically evaluate our method on the problem of automatically generating Wikipedia disambiguation pages using human generated clusterings as the ground truth. We find that our framework improves upon the baselines according to the F1 score and Entropy that are used as standard metrics to evaluate the hierarchical clustering.
A Framework for Task-specific Short Document Expansion
Authors: Ramakrishna B. Bairi, Raghavendra Udupa, Ganesh Ramakrishnan
Venue: CIKM
Year: 2016

Collections that contain a large number of short texts are becoming increasingly common (eg., tweets, reviews, etc). Analytical tasks (such as classification, clustering, etc.) involving short texts could be challenging due to the lack of context and owing to their sparseness. An often encountered problem is low accuracy on the task. A standard technique used in the handling of short texts is expanding them before subjecting them to the task. However, existing works on short text expansion suffer from certain limitations: (i) they depend on domain knowledge to expand the text; (ii) they employ task-specific heuristics; and (iii) the expansion procedure is tightly coupled to the task. This makes it hard to adapt a procedure, designed for one task, into another. We present an expansion technique -- TIDE (Task-specIfic short Document Expansion) -- that can be applied on several Machine Learning, NLP and Information Retrieval tasks on short texts (such as short text classification, clustering, entity disambiguation, and the like) without using task specific heuristics and domain-specific knowledge for expansion. At the same time, our technique is capable of learning to expand short texts in a task-specific way. That is, the same technique that is applied to expand a short text in two different tasks is able to learn to produce different expansions depending upon what expansion benefits the task's performance. To speed up the learning process, we also introduce a technique called block learning. Our experiments with classification and clustering tasks show that our framework improves upon several baselines according to the standard evaluation metrics which includes the accuracy and normalized mutual information (NMI).
Sequential Query Expansion using Concept Graph
Authors: Saeid Balaneshinkordan, Alexander Kotov
Venue: CIKM
Year: 2016

Manually and automatically constructed concept graphs (or semantic networks), in which the nodes correspond to words or phrases and the typed edges designate semantic relationships between words and phrases, have been previously shown to be rich sources of effective latent concepts for query expansion. However, finding good expansion concepts for a given query in large and dense concept graphs is a challenging problem, since the number of candidate concepts that are related to query terms and phrases and need to be examined increases exponentially with the distance from the original query concepts. In this paper, we propose a two-stage feature-based method for sequential selection of the most effective concepts for query expansion from a concept graph. In the first stage, the proposed method weighs the concepts according to different types of computationally inexpensive features, including collection and concept graph statistics. In the second stage, a sequential concept selection algorithm utilizing more expensive features is applied to find the most effective expansion concepts at different distances from the original query concepts. Experiments on TREC datasets of different type indicate that the proposed method achieves significant improvement in retrieval accuracy over state-of-the-art methods for query expansion using concept graphs.
Topological Graph Sketching for Incremental and Scalable Analytics
Authors: Bortik Bandyopadhyay, David Fuhry, Aniket Chakrabarti, Srinivasan Parthasarathy
Venue: CIKM
Year: 2016

We propose a novel, scalable, and principled graph sketching technique based on minwise hashing of local neighborhood. For an n-node graph with e-edges (e >> n), we incrementally maintain in real-time a minwise neighbor sampled subgraph using k hash functions in O(n x k) memory, limit being user-configurable by the parameter k. Symmetrization and similarity based techniques can recover from these data structures a significant portion of the original graph. We present theoretical analysis of the minwise sampling strategy and also derive unbiased estimators for important graph properties such as triangle count and neighborhood overlap.
Active Content-Based Crowdsourcing Task Selection
Authors: Piyush Bansal, Carsten Eickhoff, Thomas Hofmann
Venue: CIKM
Year: 2016

Crowdsourcing has long established itself as a viable alternative to corpus annotation by domain experts for tasks such as document relevance assessment. The crowdsourcing process traditionally relies on high degrees of label redundancy in order to mitigate the detrimental effects of individually noisy worker submissions. Such redundancy comes at the cost of increased label volume, and, subsequently, monetary requirements. In practice, especially as the size of datasets increases, this is undesirable. In this paper, we focus on an alternate method that exploits document information instead, to infer relevance labels for unjudged documents. We present an active learning scheme for document selection that aims at maximising the overall relevance label prediction accuracy, for a given budget of available relevance judgements by exploiting system-wide estimates of label variance and mutual information.
Modeling and Predicting Popularity Dynamics via an Influence-based Self-Excited Hawkes Process
Authors: Peng Bao
Venue: CIKM
Year: 2016

Modeling and predicting the popularity dynamics of individual user generated items on online social networks has important implications in a wide range of areas. The challenge of this problem comes from the inequality of the popularity of content and the numerous complex factors. Existing works mainly focus on exploring relevant factors for prediction and fitting the time series of popularity dynamics into certain class of functions, while ignoring the underlying arrival process of attentions. Also, the exogenous effect of user activity variation on the platform has been neglected. In this paper, we propose a probabilistic model using an influence-based self-excited Hawkes process (ISEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures three ingredients: the intrinsic attractiveness of a microblog with exponential time decay, the user-specific triggering effect of each forwardings based on the endogenous influence among users, and the exogenous effect from the platform. We validate the ISEHP model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that our proposed model consistently outperforms existing prediction models.
Optimizing Nugget Annotations with Active Learning
Authors: Gaurav Baruah, Haotian Zhang, Rakesh Guttikonda, Jimmy Lin, Mark D. Smucker, Olga Vechtomova
Venue: CIKM
Year: 2016

Nugget-based evaluations, such as those deployed in the TREC Temporal Summarization and Question Answering tracks, require human assessors to determine whether a nugget is present in a given piece of text. This process, known as nugget annotation, is labor-intensive. In this paper, we present two active learning techniques that prioritize the sequence in which candidate nugget/sentence pairs are presented to an assessor, based on the likelihood that the sentence contains a nugget. Our approach builds on the recognition that nugget annotation is similar to high-recall retrieval, and we adapt proven existing solutions. Simulation experiments with four existing TREC test collections show that our techniques yield far more matches for a given level of effort than baselines that are typically deployed in previous nugget-based evaluations.
Approximate Discovery of Functional Dependencies for Large Datasets
Authors: Tobias Bleifuß, Susanne Bülow, Johannes Frohnhofen, Julian Risch, Georg Wiese, Sebastian Kruse, Thorsten Papenbrock, Felix Naumann
Venue: CIKM
Year: 2016

Functional dependencies (FDs) are an important prerequisite for various data management tasks, such as schema normalization, query optimization, and data cleansing. However, automatic FD discovery entails an exponentially growing search and solution space, so that even today's fastest FD discovery algorithms are limited to small datasets only, due to long runtimes and high memory consumptions.
Balanced Supervised Non-Negative Matrix Factorization for Childhood Leukaemia Patients
Authors: Ali Braytee, Daniel R. Catchpoole, Paul J. Kennedy, Wei Liu
Venue: CIKM
Year: 2016

Supervised feature extraction methods have received considerable attention in the data mining community due to their capability to improve the classification performance of the unsupervised dimensionality reduction methods. With increasing dimensionality, several methods based on supervised feature extraction are proposed to achieve a feature ranking especially on microarray gene expression data. This paper proposes a method with twofold objectives: it implements a balanced supervised non-negative matrix factorization (BSNMF) to handle the class imbalance problem in supervised non-negative matrix factorization techniques. Furthermore, it proposes an accurate gene ranking method based on our proposed BSNMF for microarray gene expression datasets. To the best of our knowledge, this is the first work to handle the class imbalance problem in supervised feature extraction methods. This work is part of a Human Genome project at The Children's Hospital at Westmead (TB-CHW), Australia. Our experiments indicate that the factorized components using supervised feature extraction approach have more classification capability than the unsupervised one, but it drastically fails at the presence of class imbalance problem. Our proposed method outperforms the state-of-the-art methods and shows promise in overcoming this concern.
A Personal Perspective and Retrospective on Web Search Technology
Authors: Andrei Z. Broder
Venue: CIKM
Year: 2016

This talk is a review of some Web research and predictions that I co-authored over the last two decades: both what turned out gratifyingly right and what turned out embarrassingly wrong. Topics will include near-duplicates, the Web graph, query intent, inverted indices efficiency, and others. While this seems a completely idiosyncratic collection there are in fact concealed connections that offer good clues to the big question: what will happen next?
Reuse-based Optimization for Pig Latin
Authors: Jesús Camacho-Rodríguez, Dario Colazzo, Melanie Herschel, Ioana Manolescu, Soudip Roy Chowdhury
Venue: CIKM
Year: 2016

Pig Latin is a popular language which is widely used for parallel processing of massive data sets. Currently, subexpressions occurring repeatedly in Pig Latin scripts are executed as many times as they appear, and the current Pig Latin optimizer does not identify reuse opportunities. We present a novel optimization approach aiming at identifying and reusing repeated subexpressions in Pig Latin scripts. Our optimization algorithm, named PigReuse, identifies subexpression merging opportunities, selects the best ones to execute based on a cost function, and reuses their results as needed in order to compute exactly the same output as the original scripts. Our experiments demonstrate the effectiveness of our approach.
The Fourth International Workshop on Social Web for Disaster Management (SWDM 2016)
Authors: Carlos Castillo, Fernando Diaz, Yu-Ru Lin, Jie Yin
Venue: CIKM
Year: 2016

The proliferation of social media platforms together with the wide adoption of smartphone devices has transformed how we communicate and share news. During large-scale emergencies, such as natural disasters or armed attacks, victims, responders, and volunteers increasingly use social media to post situation updates and to request and offer help. The use of social media for emergency and disaster response has been a prominent application of information and knowledge management techniques in recent years. There are a number of challenges associated with near real-time processing of vast volumes of information in a way that makes sense for people directly affected, for volunteer organizations, and for official emergency response agencies. As massive amount of messages posted by users are transformed into semi-structured records via information extraction and natural language processing techniques, there is a growing need for developing advanced techniques to aggregate this large-scale data to gain an understanding of the ``big picture'' of an emergency, and to detect and predict how a disaster could develop. This workshop seeks to provide a platform for the exchange of ideas, identification of important problems, and discovery of possible synergies. It will enable interesting discussions and encouraged collaboration between various disciplines, and information and knowledge management approaches is the core of this workshop.
On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data
Authors: Prasad Cheema, Nguyen Lu Dang Khoa, Mehrisadat Makki Alamdari, Wei Liu, Yang Wang, Fang Chen, Peter Runcie
Venue: CIKM
Year: 2016

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.
Group-Aware Weighted Bipartite B-Matching
Authors: Cheng Chen, Sean Chester, Venkatesh Srinivasan, Kui Wu, Alex Thomo
Venue: CIKM
Year: 2016

The weighted bipartite B-matching (WBM) problem models a host of data management applications, ranging from recommender systems to Internet advertising and e-commerce. Many of these applications, however, demand versatile assignment constraints, which WBM is weak at modelling.
From Recommendation to Profile Inference (Rec2PI): A Value-added Service to Wi-Fi Data Mining
Authors: Cheng Chen, Fang Dong, Kui Wu, Venkatesh Srinivasan, Alex Thomo
Venue: CIKM
Year: 2016

Portable smart devices have become prevalent and are used for ubiquitous access to the Internet in our daily life. Taking advantage of this trend, brick-and-mortar retailers have been increasingly deploying free Wi-Fi hotspots to provide easy Internet access for their customers. This opens the opportunity for retailers to collect customer information and perform data mining to improve the quality of their service. In this paper, we propose a novel value-added service to Wi-Fi data mining, Rec2PI, which can infer users' preference profiles based on recommendations pushed by third-party apps. Such profiles can be used to improve users' online experience and enable a brick-and-mortar retailer to participate in the global advertising business. Since the goal and technical difficulties of Rec2PI significantly differ from those of traditional recommender systems, we present a general framework of Rec2PI to illustrate its process. To tackle the technical challenges in profile inference, we propose novel algorithms built using copulas, a statistical tool suitable for capturing complex dependence structure beyond the scope of linear dependence. In the context of rating-based recommendations, we evaluate the proposed algorithms using an open dataset and a real-world recommender system. The evaluation results show that Rec2PI creates consistent and accurate inference results.
Efficient Batch Processing for Multiple Keyword Queries on Graph Data
Authors: Lu Chen, Chengfei Liu, Xiaochun Yang, Bin Wang, Jianxin Li, Rui Zhou
Venue: CIKM
Year: 2016

Recently, answering keyword queries on graph data has drawn a great deal of attention from database communities. However, most graph keyword search solutions proposed so far primarily focus on a single query setting. We observe that for a popular keyword query system, the number of keyword queries received could be substantially large even in a short time interval, and the chance that these queries share common keywords is quite high. Therefore, answering keyword queries in batches would significantly enhance the performance of the system. Motivated by this, this paper studies efficient batch processing for multiple keyword queries on graph data. Realized that finding both the optimal query plan for multiple queries and the optimal query plan for a single keyword query on graph data are computationally hard, we first propose two heuristic approaches which target maximizing keyword overlap and give preferences for processing keywords with short sizes. Then we devise a cardinality based cost estimation model that takes both graph data statistics and search semantics into account. Based on the model, we design an A* based algorithm to find the global optimal execution plan for multiple queries. We evaluate the proposed model and algorithms on two real datasets and the experimental results demonstrate their efficacy.
Efficient Computation of Importance Based Communities in Web-Scale Networks Using a Single Machine
Authors: Shu Chen, Ran Wei, Diana Popova, Alex Thomo
Venue: CIKM
Year: 2016

Finding decompositions of a graph into a family of communities is crucial to understanding its underlying structure. Algorithms for finding communities in networks often rely only on structural information and search for cohesive subsets of nodes. In practice however, we would like to find communities that are not only cohesive, but also influential or important. In order to capture such communities, Li, Qin, Yu, and Mao introduced a novel community model called "k-influential community" based on the concept of $k$-core, with numerical values representing "influence" assigned to the nodes. They formulate the problem of finding the top-r most important communities as finding r connected k-core subgraphs ordered by the lower-bound of their importance. In this paper, our goal is to scale-up the computation of top-r, k-core communities to web-scale graphs of tens of billions of edges. We feature several fast new algorithms for this problem. With our implementations, we show that we can efficiently handle massive networks using a single consumer-level machine within a reasonable amount of time.