OpenRaiser/Intern-Atlas huggingface dataset:
A 2-poisson model for probabilistic coreference of named entities for improved text retrieval
Authors: Seung-Hoon Na, Hwee Tou Ng
Venue: Conference SIGIR 2009
Year: 2009

Text retrieval queries frequently contain named entities. The standard approach of term frequency weighting does not work well when estimating the term frequency of a named entity, since anaphoric expressions (like he, she, the movie, etc) are frequently used to refer to named entities in a document, and the use of anaphoric expressions causes the term frequency of named entities to be underestimated. In this paper, we propose a novel 2-Poisson model to estimate the frequency of anaphoric expressions of a named entity, without explicitly resolving the anaphoric expressions. Our key assumption is that the frequency of anaphoric expressions is distributed over named entities in a document according to the probabilities of whether the document is elite for the named entities. This assumption leads us to formulate our proposed Co-referentially Enhanced Entity Frequency (CEEF). Experimental results on the text collection of TREC Blog Track show that CEEF achieves significant and consistent improvements over state-of-the-art retrieval methods using standard term frequency estimation. In particular, we achieve a 3% increase of MAP over the best performing run of TREC 2008 Blog Track.
mStruct: a new admixture model for inference of population structure in light of both genetic admixing and allele mutations
Authors: Suyash Shringarpure, Eric P. Xing
Venue: Conference ICML 2008
Year: 2008

Traditional methods for analyzing population structure, such as the Structure program, ignore the influence of mutational effects. We propose mStruct, an admixture of population-specific mixtures of inheritance models, that addresses the task of structure inference and mutation estimation jointly through a hierarchical Bayesian framework, and a variational algorithm for inference. We validated our method on synthetic data, and used it to analyze the HGDP-CEPH cell line panel of microsatellites used in (Rosenberg et al., 2002) and the HGDP SNP data used in (Conrad et al., 2006). A comparison of the structural maps of world populations estimated by mStruct and Structure is presented, and we also report potentially interesting mutation patterns in world populations estimated by mStruct, which is not possible by Structure.
Clustering the tagged web
Authors: Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina
Venue: Conference WSDM 2009
Year: 2009

Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from large-scale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase.
Graph-based text classification: learn from your neighbors
Authors: Ralitsa Angelova, Gerhard Weikum
Venue: Conference SIGIR 2006
Year: 2006

Automatic classification of data items, based on training samples, can be boosted by considering the neighborhood of data items in a graph structure (e.g., neighboring documents in a hyperlink environment or co-authors and their publications for bibliographic data entries). This paper presents a new method for graph-based classification, with particular emphasis on hyperlinked text documents but broader applicability. Our approach is based on iterative relaxation labeling and can be combined with either Bayesian or SVM classifiers on the feature spaces of the given data items. The graph neighborhood is taken into consideration to exploit locality patterns while at the same time avoiding overfitting. In contrast to prior work along these lines, our approach employs a number of novel techniques: dynamically inferring the link/class pattern in the graph in the run of the iterative relaxation labeling, judicious pruning of edges from the neighborhood graph based on node dissimilarities and node degrees, weighting the influence of edges based on a distance metric between the classification labels of interest and weighting edges by content similarity measures. Our techniques considerably improve the robustness and accuracy of the classification outcome, as shown in systematic experimental comparisons with previously published methods on three different real-world datasets.
A word clustering approach for language model-based sentence retrieval in question answering systems
Authors: Saeedeh Momtazi, Dietrich Klakow
Venue: Conference CIKM 2009
Year: 2009

In this paper we propose a term clustering approach to improve the performance of sentence retrieval in Question Answering (QA) systems. As the search in question answering is conducted over smaller segments of data than in a document retrieval task, the problems of data sparsity and exact matching become more critical. In this paper we propose Language Modeling (LM) techniques to overcome such problems and improve the sentence retrieval performance.
Named entity normalization in user generated content
Authors: Valentin Jijkoun, Mahboob Alam Khalid, Maarten Marx, Maarten de Rijke
Venue: Conference SIGIR 2008
Year: 2008

Named entity recognition is important for semantically oriented retrieval tasks, such as question answering, entity retrieval, biomedical retrieval, trend detection, and event and entity tracking. In many of these tasks it is important to be able to accurately normalize the recognized entities, i.e., to map surface forms to unambiguous references to real world entities. Within the context of structured databases, this task (known as record linkage and data de-duplication) has been a topic of active research for more than five decades. For edited content, such as news articles, the named entity normalization (NEN) task is one that has recently attracted considerable attention. We consider the task in the challenging context of user generated content (UGC), where it forms a key ingredient of tracking and media-analysis systems.
A novel scheme for domain-transfer problem in the context of sentiment analysis
Authors: Songbo Tan, Gaowei Wu, Huifeng Tang, Xueqi Cheng
Venue: Conference CIKM 2007
Year: 2007

In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain.
Effects of user similarity in social media
Authors: Ashton Anderson, Daniel P. Huttenlocher, Jon M. Kleinberg, Jure Leskovec
Venue: Conference WSDM 2012
Year: 2012

There are many settings in which users of a social media application provide evaluations of one another. In a variety of domains, mechanisms for evaluation allow one user to say whether he or she trusts another user, or likes the content they produced, or wants to confer special levels of authority or responsibility on them. Earlier work has studied how the relative status between two users - that is, their comparative levels of status in the group - affects the types of evaluations that one user gives to another.
Retrieval and feedback models for blog feed search
Authors: Jonathan L. Elsas, Jaime Arguello, Jamie Callan, Jaime G. Carbonell
Venue: Conference SIGIR 2008
Year: 2008

Blog feed search poses different and interesting challenges from traditional ad hoc document retrieval. The units of retrieval, the blogs, are collections of documents, the blog posts. In this work we adapt a state-of-the-art federated search model to the feed retrieval task, showing a significant improvement over algorithms based on the best performing submissions in the TREC 2007 Blog Distillation task[12]. We also show that typical query expansion techniques such as pseudo-relevance feedback using the blog corpus do not provide any significant performance improvement and in many cases dramatically hurt performance. We perform an in-depth analysis of the behavior of pseudo-relevance feedback for this task and develop a novel query expansion technique using the link structure in Wikipedia. This query expansion technique provides significant and consistent performance improvements for this task, yielding a 22% and 14% improvement in MAP over the unexpanded query for our baseline and federated algorithms respectively.
Mining term association patterns from search logs for effective query reformulation
Authors: Xuanhui Wang, ChengXiang Zhai
Venue: Conference CIKM 2008
Year: 2008

Search engine logs are an emerging new type of data that offers interesting opportunities for data mining. Existing work on mining such data has mostly attempted to discover knowledge at the level of queries (e.g., query clusters). In this paper, we propose to mine search engine logs for patterns at the level of terms through analyzing the relations of terms inside a query. We define two novel term association patterns (i.e., context-sensitive term substitutions and term additions) and propose new methods for mining such patterns from search engine logs. These two patterns can be used to address the mis-specification and under-specification problems of ineffective queries. Experiment results on real search engine logs show that the mined context-sensitive term substitutions can be used to effectively reword queries and improve their accuracy, while the mined context-sensitive term addition patterns can be used to support query refinement in a more effective way.
TopicVis: a GUI for topic-based feedback and navigation
Authors: Debasis Ganguly, Manisha Ganguly, Johannes Leveling, Gareth J. F. Jones
Venue: Conference SIGIR 2013
Year: 2013

This paper describes a search system which includes topic model visualization to improve the user search experience. The system graphically renders the topics in a retrieved set of documents, enables a user to selectively refine search results and allows easy navigation through information on selective topics within documents.
Personalized time-aware tweets summarization
Authors: Zhaochun Ren, Shangsong Liang, Edgar Meij, Maarten de Rijke
Venue: Conference SIGIR 2013
Year: 2013

We focus on the problem of selecting meaningful tweets given a user's interests; the dynamic nature of user interests, the sheer volume, and the sparseness of individual messages make this an challenging problem. Specifically, we consider the task of time-aware tweets summarization, based on a user's history and collaborative social influences from ``social circles.'' We propose a time-aware user behavior model, the Tweet Propagation Model (TPM), in which we infer dynamic probabilistic distributions over interests and topics. We then explicitly consider novelty, coverage, and diversity to arrive at an iterative optimization algorithm for selecting tweets. Experimental results validate the effectiveness of our personalized time-aware tweets summarization method based on TPM.
The Wisdom of Advertisers: Mining Subgoals via Query Clustering
Authors: Takehiro Yamamoto, Tetsuya Sakai, Mayu Iwata, Chen Yu, Ji-Rong Wen, Katsumi Tanaka
Venue: Conference CIKM 2012
Year: 2012

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.
Lambdamerge: merging the results of query reformulations
Authors: Daniel Sheldon, Milad Shokouhi, Martin Szummer, Nick Craswell
Venue: Conference WSDM 2011
Year: 2011

Search engines can automatically reformulate user queries in a variety of ways, often leading to multiple queries that are candidates to replace the original. However, selecting a replacement can be risky: a reformulation may be more effective than the original or significantly worse, depending on the nature of the query, the source of reformulation candidates, and the corpus. In this paper, we explore methods to mitigate this risk by issuing several versions of the query (including the original) and merging their results. We focus on reformulations generated by random walks on the click graph, a method that can produce very good reformulations but is also variable and prone to topic drift. Our primary contribution is λ-Merge, a supervised merging method that is trained to directly optimize a retrieval metric (such as NDCG or MAP) using features that describe both the reformulations and the documents they return. In experiments on Bing data and GOV2, λ-Merge outperforms the original query and several unsupervised merging methods. λ-Merge also outperforms a supervised method to predict and select the best single formulation, and is competitive with an oracle that always selects the best formulation.
Opinion Digger: An unsupervised opinion miner from unstructured product reviews
Authors: Samaneh Moghaddam, Martin Ester
Venue: Conference CIKM 2010
Year: 2010

Mining customer reviews (opinion mining) has emerged as an interesting new research direction. Most of the reviewing websites such as Epinions.com provide some additional information on top of the review text and overall rating, including a set of predefined aspects and their ratings, and a rating guideline which shows the intended interpretation of the numerical ratings. However, the existing methods have ignored this additional information. We claim that using this information, which is freely available, along with the review text can effectively improve the accuracy of opinion mining. We propose an unsupervised method, called Opinion Digger, which extracts important aspects of a product and determines the overall consumer's satisfaction for each, by estimating a rating in the range from 1 to 5. We demonstrate the improved effectiveness of our methods on a real life dataset that we crawled from Epinions.com.
Exploiting paths for entity search in rdf graphs
Authors: Minsuk Kahng, Sang-goo Lee
Venue: Conference SIGIR 2012
Year: 2012

The field of entity search using Semantic Web (RDF) data has gained more interest recently. In this paper, we propose a probabilistic entity retrieval model for RDF graphs using paths in the graph. Unlike previous work which assumes that all descriptions of an entity are directly linked to the entity node, we assume that an entity can be described with any node that can be reached from the entity node by following paths in the RDF graph. Our retrieval model simulates the generation process of query terms from an entity node by traversing the graph. We evaluate our approach using a standard evaluation framework for entity search.
Combining inverted indices and structured search for ad-hoc object retrieval
Authors: Alberto Tonon, Gianluca Demartini, Philippe Cudré-Mauroux
Venue: Conference SIGIR 2012
Year: 2012

Retrieving semi-structured entities to answer keyword queries is an increasingly important feature of many modern Web applications. The fast-growing Linked Open Data (LOD) movement makes it possible to crawl and index very large amounts of structured data describing hundreds of millions of entities. However, entity retrieval approaches have yet to find efficient and effective ways of ranking and navigating through those large data sets. In this paper, we address the problem of Ad-hoc Object Retrieval over large-scale LOD data by proposing a hybrid approach that combines IR and structured search techniques. Specifically, we propose an architecture that exploits an inverted index to answer keyword queries as well as a semi-structured database to improve the search effectiveness by automatically generating queries over the LOD graph. Experimental results show that our ranking algorithms exploiting both IR and graph indices outperform state-of-the-art entity retrieval techniques by up to 25% over the BM25 baseline.
Succinct queries for linking and tracking news in social media
Authors: Luchen Tan, Charles L. A. Clarke
Venue: Conference CIKM 2014
Year: 2014

Given a current news article, we wish to create a succinct query reflecting its content, which may be used to follow the news story over a period of days, or even weeks. In part, the need for succinct queries is occasioned by limitations of commercial social media search engines, which can perform poorly with longer queries. We start by applying established key phrase extraction methods to the article, creating an initial set of candidate query terms. We then generate a series of probe queries, each a subset of these candidate terms, which we apply to search current social media streams. By analyzing the results of these probes, we rank and trim the candidate set to create a succinct query. We present an experimental study of this method based on a collection of news articles taken from March-April 2014, with the resulting succinct queries used to re-query social media one week later.
Dynamic texture recognition
Authors: Payam Saisan, Gianfranco Doretto, Ying Nian Wu, Stefano Soatto
Venue: Conference CVPR 2001
Year: 2001

Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is non-linear, a distance between models must be defined We examine three different distances in the space of autoregressive models and assess their power.
Yali: a crowdsourcing plug-in for NERD
Authors: Yafang Wang, Lili Jiang, Johannes Hoffart, Gerhard Weikum
Venue: Conference SIGIR 2013
Year: 2013

We demonstrate the YaLi browser plug-in which discovers named entities in Web pages and provides background knowledge about them. The plug-in is implemented with two purposes. From a user perspective, it enriches the browsing experience with entities, helping users with their information needs. From the research perspective, we aim to improve the methods that are used for named entity recognition and disambiguation (NERD) by leveraging the plug-in as an implicit crowdsourcing platform. YaLi tracks the system's errors and the users' corrections, and also gathers implicit training data for improving NERD accuracy.
Understanding evolution of research themes: a probabilistic generative model for citations
Authors: Xiaolong Wang, Chengxiang Zhai, Dan Roth
Venue: Conference KDD 2013
Year: 2013

Understanding how research themes evolve over time in a research community is useful in many ways (e.g., revealing important milestones and discovering emerging major research trends). In this paper, we propose a novel way of analyzing literature citation to explore the research topics and the theme evolution by modeling article citation relations with a probabilistic generative model. The key idea is to represent a research paper by a ``bag of citations'' and model such a ``citation document'' with a probabilistic topic model. We explore the extension of a particular topic model, i.e., Latent Dirichlet Allocation~(LDA), for citation analysis, and show that such a Citation-LDA can facilitate discovering of individual research topics as well as the theme evolution from multiple related topics, both of which in turn lead to the construction of evolution graphs for characterizing research themes. We test the proposed citation-LDA on two datasets: the ACL Anthology Network(AAN) of natural language research literatures and PubMed Central(PMC) archive of biomedical and life sciences literatures, and demonstrate that Citation-LDA can effectively discover the evolution of research themes, with better formed topics than (conventional) Content-LDA.
Structural link analysis and prediction in microblogs
Authors: Dawei Yin, Liangjie Hong, Brian D. Davison
Venue: Conference CIKM 2011
Year: 2011

With hundreds of millions of participants, social media services have become commonplace. Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and predicting new links are important for many tasks such as friend recommendation, community detection, and modeling network growth. We note that the link prediction problem in a hybrid network is different from previously studied networks. Unlike the information networks and traditional online social networks, the structures in a hybrid network are more complicated and informative. We compare most popular and recent methods and principles for link prediction and recommendation. Finally we propose a novel structure-based personalized link prediction model and compare its predictive performance against many fundamental and popular link prediction methods on real-world data from the Twitter microblogging network. Our experiments on both static and dynamic data sets show that our methods noticeably outperform the state-of-the-art.
Online debate summarization using topic directed sentiment analysis
Authors: Sarvesh Ranade, Jayant Gupta, Vasudeva Varma, Radhika Mamidi
Venue: Conference KDD 2013
Year: 2013

Social networking sites provide users a virtual community interaction platform to share their thoughts, life experiences and opinions. Online debate forum is one such platform where people can take a stance and argue in support or opposition of debate topics. An important feature of such forums is that, they are dynamic and increase rapidly. In such situations, effective opinion summarization approaches are needed so that readers need not go through the entire debate. This paper aims to summarize online debates by extracting highly topic relevant and sentiment rich sentences. The proposed approach takes into account topic relevant, document relevant and sentiment based features to capture topic opinionated sentences. ROUGE scores are used to evaluate our system. Our system significantly outperforms several baseline systems and show 5.2% (ROUGE-1), 7.3% (ROUGE-2) and 5.5% (ROUGE-L) improvement over the state-of-the-art opinion summarization system. The results verify that topic directed sentiment features are most important to generate effective debate summaries.