OpenRaiser/Intern-Atlas huggingface dataset:
Information extraction as a filtering task
Authors: Henning Wachsmuth, Benno Stein, Gregor Engels
Venue: CIKM
Year: 2013

Information extraction is usually approached as an annotation task: Input texts run through several analysis steps of an extraction process in which different semantic concepts are annotated and matched against the slots of templates. We argue that such an approach lacks an efficient control of the input of the analysis steps. In this paper, we hence propose and evaluate a model and a formal approach that consistently put the filtering view in the focus: Before spending annotation effort, filter those portions of the input texts that may contain relevant information for filling a template and discard the others. We model all dependencies between the semantic concepts sought for with a truth maintenance system, which then efficiently infers the portions of text to be annotated in each analysis step. The filtering view enables an information extraction system (1) to annotate only relevant portions of input texts and (2) to easily trade its run-time efficiency for its recall. We provide our approach as an open-source extension of Apache UIMA and we show the potential of our approach in a number of experiments.
Locality sensitive hashing revisited: filling the gap between theory and algorithm analysis
Authors: Hongya Wang, Jiao Cao, LihChyun Shu, Davood Rafiei
Venue: CIKM
Year: 2013

Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to similarity search in high-dimensional spaces. Based on LSH, a considerable number of nearest neighbor search algorithms have been proposed in the past, with some of them having been used in many real-life applications. Apart from their demonstrated superior performance in practice, the popularity of the LSH algorithms is mainly due to their provable performance bounds on query cost, space consumption and failure probability.
Towards metric fusion on multi-view data: a cross-view based graph random walk approach
Authors: Yang Wang, Xuemin Lin, Qing Zhang
Venue: CIKM
Year: 2013

Many real-world objects described by multiple attributes or features can be decomposed as multiple "views" (e.g., an image can be described by a color view or a shape view), which often provides complementary information to each other. Learning a metric (similarity measures) for multi-view data is primary due to its wide applications in practices. However, leveraging multi-view information to produce a good metric is a great challenge and existing techniques are concerned with pairwise similarities, leading to undesirable fusion metric and high computational complexity. In this paper, we propose a novel Metric Fusion technique via cross-view graph Random Walk, named MFRW, regarding a multi-view based similarity graphs (with each similarity graph constructed under each view). Instead of using pairwise similarities, we seek a high-order metric yielded by graph random walks over constructed similarity graphs. Observing that ``outlier views" may exist in the fusion process, we incorporate the coefficient matrices representing the correlation strength between any two views into MFRW, named WMFRW. The principle of \textsf{WMFRW} is implemented by exploring the ``common latent structure" between views. The empirical studies conducted on real-world databases demonstrate that our approach outperforms the state-of-the-art competitors in terms of effectiveness and efficiency.
Programming with personalized pagerank: a locally groundable first-order probabilistic logic
Authors: William Yang Wang, Kathryn Mazaitis, William W. Cohen
Venue: CIKM
Year: 2013

Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realistically-sized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query---i.e., mapping it to a propositional representation, and then performing propositional inference---and with a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: in particular, every query $Q$ can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well on an entity resolution task, a classification task, and a joint inference task; that the cost of inference is independent of database size; and that speedup in learning is possible by multi-threading.
Content coverage maximization on word networks for hierarchical topic summarization
Authors: Chi Wang, Xiao Yu, Yanen Li, Chengxiang Zhai, Jiawei Han
Venue: CIKM
Year: 2013

This paper studies text summarization by extracting hierarchical topics from a given collection of documents. We propose a new approach of text modeling via network analysis. We convert documents into a word influence network, and find the words summarizing the major topics with an efficient influence maximization algorithm. Besides, the influence capability of the topic words on other words in the network reveal the relations among the topic words. Then we cluster the words and build hierarchies for the topics. Experiments on large collections of Web documents show that a simple method based on the influence analysis is effective, compared with existing generative topic modeling and random walk based ranking.
Weighted hashing for fast large scale similarity search
Authors: Qifan Wang, Dan Zhang, Luo Si
Venue: CIKM
Year: 2013

Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency.
PLEAD 2013: politics, elections and data
Authors: Ingmar Weber, Ana-Maria Popescu, Marco Pennacchiotti
Venue: CIKM
Year: 2013

What is the role of the internet in politics general and during campaigns in particular? And what is the role of large amounts of user data in all of this?
Inside the world's playlist
Authors: Wouter Weerkamp, Manos Tsagkias, Maarten de Rijke
Venue: CIKM
Year: 2013

We describe Streamwatchr, a real-time system for analyzing the music listening behavior of people around the world. Streamwatchr collects music-related tweets, extracts artists and songs, and visualizes the results in three ways: (i) currently trending songs and artists, (ii) newly discovered songs, and (iii) popularity statistics per country and world-wide for both songs and artists.
Overlapping community detection using seed set expansion
Authors: Joyce Jiyoung Whang, David F. Gleich, Inderjit S. Dhillon
Venue: CIKM
Year: 2013

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. One of the most successful techniques for finding overlapping communities is based on local optimization and expansion of a community metric around a seed set of vertices. In this paper, we propose an efficient overlapping community detection algorithm using a seed set expansion approach. In particular, we develop new seeding strategies for a personalized PageRank scheme that optimizes the conductance community score. The key idea of our algorithm is to find good seeds, and then expand these seed sets using the personalized PageRank clustering procedure. Experimental results show that this seed set expansion approach outperforms other state-of-the-art overlapping community detection methods. We also show that our new seeding strategies are better than previous strategies, and are thus effective in finding good overlapping clusters in a graph.
Effective measures for inter-document similarity
Authors: John S. Whissell, Charles L. A. Clarke
Venue: CIKM
Year: 2013

While supervised learning-to-rank algorithms have largely supplanted unsupervised query-document similarity measures for search, the exploration of query-document measures by many researchers over many years produced insights that might be exploited in other domains. For example, the BM25 measure substantially and consistently outperforms cosine across many tested environments, and potentially provides retrieval effectiveness approaching that of the best learning-to-rank methods over equivalent features sets. Other measures based on language modeling and divergence from randomness can outperform BM25 in some circumstances. Despite this evidence, cosine remains the prevalent method for determining inter-document similarity for clustering and other applications. However, recent research demonstrates that BM25 terms weights can significantly improve clustering. In this work, we extend that result, presenting and evaluating novel inter-document similarity measures based on BM25, language modeling, and divergence from randomness. In our first experiment we analyze the accuracy of nearest neighborhoods when using our measures. In our second experiment, we analyze using clustering algorithms in conjunction with our measures. Our novel symmetric BM25 and language modeling similarity measures outperform alternative measures in both experiments. This outcome strongly recommends the adoption of these measures, replacing cosine similarity in future work.
Toward advice mining: conditional random fields for extracting advice-revealing text units
Authors: Alfan Farizki Wicaksono, Sung-Hyon Myaeng
Venue: CIKM
Year: 2013

Web forums are platforms for personal communications on sharing information with others. Such information is often expressed in the form of advice. In this paper, we address the problem of advice-revealing text unit (ATU) extraction from online forums due to its usefulness in travel domain. We represent advice as a two-tuple comprising an advice-revealing sentence and its context sentences. To extract the advice-revealing sentences, we propose to define the task as a sequence labeling problem, using three different types of features: syntactic, contextual, and semantic features. To extract the context sentences, we propose to use a 2 Dimensional CRF (2D-CRF) model, which gives the best performance compared to traditional machine learning models. Finally, we present a solution to the integrated problem of extracting both advice-revealing sentences and their respective context sentences at the same time using our proposed models, i.e., Multiple Linear CRF (ML-CRF) and 2 Dimensional CRF Plus (2D-CRF+). The experimental results show that ML-CRF performs better than any other models studied in this paper for extracting advice-revealing sentences and context sentences.
Challenges in commerce search
Authors: Hugh Williams
Venue: CIKM
Year: 2013

Commerce search engines allow users to discover products, learn about them, and, importantly, make purchases. Commerce search is a challenging problem --- one that is very different to conventional text and web search. In this talk, we discuss what makes commerce search hard, how eBay has solved some of these problems, and what challenges eBay faces in the next generation of its search technologies. We also discuss the recent release of eBay's Cassini engine, share facts and figures about its scale, and outline the progress eBay has made in ranking and relevance for commerce search.
A prototype application for real-time recognition and disambiguation of clinical abbreviations
Authors: Yonghui Wu, Joshua C. Denny, S. Trent Rosenbloom, Randolph A. Miller, Dario A. Giuse, Min Song, Hua Xu
Venue: CIKM
Year: 2013

To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems "post-process" notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist. In this paper, authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (CARD) -- i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The CARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. The prototype application embodies three word sense disambiguation (WSD) methods. We evaluated the accuracy and response times of the prototype CARD system in a simulated study. Using an existing test data set of 25 commonly observed, highly ambiguous clinical abbreviations the evaluation demonstrated that the best WSD method had an accuracy of 88.8%, and a reasonable average response time of 1.6 milliseconds per each abbreviation. The study indicates potential feasibility of real-time NLP-enabled abbreviation disambiguation within clinical documentation systems.
Web news extraction via path ratios
Authors: Gong-Qing Wu, Li Li, Xuegang Hu, Xindong Wu
Venue: CIKM
Year: 2013

In addition to the news content, most web news pages also contain navigation panels, advertisements, related news links etc. These non-news items not only exist outside the news region, but are also present in the news content region. Effectively extracting the news content and filtering the noise have important effects on the follow-up activities of content management and analysis. Our extensive case studies have indicated that there exists potential relevance between web content layouts and their tag paths. Based on this observation, we design two tag path features to measure the importance of nodes: Text to tag Path Ratio (TPR) and Extended Text to tag Path Ratio (ETPR), and describe the calculation process of TPR by traversing the parsing tree of a web news page. In this paper, we present Content Extraction via Path Ratios (CEPR) - a fast, accurate and general on-line method for distinguishing news content from non-news content by the TPR/ETPR histogram effectively. In order to improve the ability of CEPR in extracting short texts, we propose a Gaussian smoothing method weighted by a tag path edit distance. This approach can enhance the importance of internal-link nodes but ignore noise nodes existing in news content. Experimental results on the CleanEval datasets and web news pages randomly selected from well-known websites show that CEPR can extract across multi-resources, multi-styles, and multi-languages. The average F and average score with CEPR is 8.69% and 14.25% higher than CETR, which demonstrates better web news extraction performance than most existing methods.
Random walk-based graphical sampling in unbalanced heterogeneous bipartite social graphs
Authors: Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok N. Choudhary, Lu Liu
Venue: CIKM
Year: 2013

We investigate sampling techniques in unbalanced heterogeneous bipartite graphs (UHBGs), which have wide applications in real world web-scale social networks. We propose random walked-based link sampling and stratified sampling for UHBGs and show that they have advantages over generic random walk samplers. In addition, each sampler's node degree distribution parameter estimator statistic is analytically derived to be used as a quality indicator. In the experiments, we apply the two sampling techniques, with a baseline node sampling method, to both synthetic and real Facebook data. The experimental results show that random walk-based stratified sampler has significant advantage over node sampler and link sampler on UHBGs.
iNewsBox: modeling and exploiting implicit feedback for building personalized news radio
Authors: Yanan Xie, Liang Chen, Kunyang Jia, Lichuan Ji, Jian Wu
Venue: CIKM
Year: 2013

Online news reading has become the major method to know about the world as web provide more information than other media like TV and radio. However, traditional online news reading interface is inconvenient for many types of people, especially for those who are disabled or taking a bus. This paper presents a mobile application iNewsBox enabling users to listen to news collected from the Internet. In order to simplify necessary interactions of getting valuable news, we also propose a framework for using implicit feedback to recommend news in this paper. Experiment shows our algorithms in iNewsBox are effective.
Local correlation detection with linearity enhancement in streaming data
Authors: Qing Xie, Shuo Shang, Bo Yuan, Chaoyi Pang, Xiangliang Zhang
Venue: CIKM
Year: 2013

This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it difficult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of reflecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which refines the results by representative trend patterns to enhance the significance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the efficiency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than $30\%$ improvement of efficiency.
A heterogenous automatic feedback semi-supervised method for image reranking
Authors: Xin-Chao Xu, Xin-Shun Xu, Yafang Wang, Xiaolin Wang
Venue: CIKM
Year: 2013

Image reranking, which aims at enhancing the quality of keyword-based image search with the help of image features, recently has become attractive in image search community. A major challenging in this task is that image's visual features do not always well reflect image's semantic meaning. Thus, reranking methods only depending on visual features cannot guarantee to obtain good results. In addition, it is well known that the visual features of an image have strong/weak correlations with its surrounding text. Thus, it is expected that a model considering both visual features and its surrounding text can perform better than those only considering visual features. Motivated by this, in this paper, we propose the HAFSRerank--Heterogenous Automatic Feedback Semi-supervised Reranking method which makes use of both visual and textual features simultaneously during reranking. Specifically, in HAFSRerank, a multigraph is firstly constructed in which each node representing an image includes visual and textual features, and the parallel edges between them are weighted by intra-modal similarity and inter-modal similarity. A heterogenous complete graph is further derived from the multigraph. Then, an automatic feedback graph-based semi-supervised learning method is proposed to propagate the reranking scores on the complete graph, which can make use of the inter-modal similarity to update the weights of heterogenous graph automatically. Finally, the result of the semi-supervised learning is used to rerank the images. The experimental results show that HAFSRerank is superior or highly competitive to some state-of-the-art graph-based reranking methods. Moreover, the proposed reranking algorithm can be well interpreted by Bayesian theory, and does not require complex search models for special queries and any additional input from users.
Annotation of clausal functional information for semantic retrieval
Authors: Hengbin Yan
Venue: CIKM
Year: 2013

The study of language functions is closely associated with the semantic and pragmatic aspects of language. While data driven approaches have been successfully applied on retrieval of functional-semantic information at the discourse level, the work at the clause level is still largely absent. In this paper, we annotate an initial corpus with Systemic Functional Linguistics, a prominent framework for the analysis of language functions at the sentence/clause level. The annotated corpus makes it possible to train a classifier to automatically classify functional processes at the clausal level. With an initial computational resource, the linking and interoperation between the two levels of functional information is now possible, giving rise to a range of potential applications in functional/semantic retrieval.
Building optimal information systems automatically: configuration space exploration for biomedical information systems
Authors: Zi Yang, Elmer Garduño, Yan Fang, Avner Maiberg, Collin McCormack, Eric Nyberg
Venue: CIKM
Year: 2013

Software frameworks which support integration and scaling of text analysis algorithms make it possible to build complex, high performance information systems for information extraction, information retrieval, and question answering; IBM's Watson is a prominent example. As the complexity and scaling of information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into an information system in order to achieve a desired or optimal level of performance on a given task. This paper presents a formal representation of the space of possible system configurations, given a set of information processing components and their parameters (configuration space) and discusses algorithmic approaches to determine the optimal configuration within a given configuration space (configuration space exploration or CSE). We introduce the CSE framework, an extension to the UIMA framework which provides a general distributed solution for building and exploring configuration spaces for information systems. The CSE framework was used to implement biomedical information systems in case studies involving over a trillion different configuration combinations of components and parameter values operating on question answering tasks from the TREC Genomics. The framework automatically and efficiently evaluated different system configurations, and identified configurations that achieved better results than prior published results.
On handling textual errors in latent document modeling
Authors: Tao Yang, Dongwon Lee
Venue: CIKM
Year: 2013

As large-scale text data become available on the Web, textual errors in a corpus are often inevitable (e.g., digitizing historic documents). Due to the calculation of frequencies of words, however, such textual errors can significantly impact the accuracy of statistical models such as the popular Latent Dirichlet Allocation (LDA) model. To address such an issue, in this paper, we propose two novel extensions to LDA (i.e., TE-LDA and TDE-LDA): (1) The TE-LDA model incorporates textual errors into term generation process; and (2) The TDE-LDA model extends TE-LDA further by taking into account topic dependency to leverage on semantic connections among consecutive words even if parts are typos. Using both real and synthetic data sets with varying degrees of "errors", our TDE-LDA model outperforms: (1) the traditional LDA model by 16%-39% (real) and 20%-63% (synthetic); and (2) the state-of-the-art N-Grams model by 11%-27% (real) and 16%-54% (synthetic).