OpenRaiser/Intern-Atlas huggingface dataset:
Transparent user models for personalization
Authors: Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas
Venue: Conference KDD 2012
Year: 2012

Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it.
An investigation of linguistic features and clustering algorithms for topical document clustering
Authors: Vasileios Hatzivassiloglou, Luis Gravano, Ankineedu Maganti
Venue: Conference SIGIR 2000
Year: 2000

We investigate four hierarchical clustering methods (single-link, complete-link, groupwise-average, and single-pass) and two linguistically motivated text features (noun phrase heads and proper names) in the context of document clustering. A statistical model for combining similarity information from multiple sources is described and applied to DARPA's Topic Detection and Tracking phase 2 (TDT2) data. This model, based on log-linear regression, alleviates the need for extensive search in order to determine optimal weights for combining input features. Through an extensive series of experiments with more than 40,000 documents from multiple news sources and modalities, we establish that both the choice of clustering algorithm and the introduction of the additional features have an impact on clustering performance. We apply our optimal combination of features to the TDT2 test data, obtaining partitions of the documents that compare favorably with the results obtained by participants in the official TDT2 competition.
Predicting Quality Flaws in User-generated Content: The Case of Wikipedia
Authors: Maik Anderka, Benno Stein, Nedim Lipka
Venue: Conference SIGIR 2012
Year: 2012

The detection and improvement of low-quality information is a key concern in Web applications that are based on user-generated content; a popular example is the online encyclopedia Wikipedia. Existing research on quality assessment of user-generated content deals with the classification as to whether the content is high-quality or low-quality. This paper goes one step further: it targets the prediction of quality flaws, this way providing specific indications in which respects low-quality content needs improvement. The prediction is based on user-defined cleanup tags, which are commonly used in many Web applications to tag content that has some shortcomings. We apply this approach to the English Wikipedia, which is the largest and most popular user-generated knowledge source on the Web. We present an automatic mining approach to identify the existing cleanup tags, which provides us with a training corpus of labeled Wikipedia articles. We argue that common binary or multiclass classification approaches are ineffective for the prediction of quality flaws and hence cast quality flaw prediction as a one-class classification problem. We develop a quality flaw model and employ a dedicated machine learning approach to predict Wikipedia's most important quality flaws. Since in the Wikipedia setting the acquisition of significant test data is intricate, we analyze the effects of a biased sample selection. In this regard we illustrate the classifier effectiveness as a function of the flaw distribution in order to cope with the unknown (real-world) flaw-specific class imbalances. The flaw prediction performance is evaluated with 10,000 Wikipedia articles that have been tagged with the ten most frequent quality flaws: provided test data with little noise, four flaws can be detected with a precision close to 1.
On the quality of inferring interests from social neighbors
Authors: Zhen Wen, Ching-Yung Lin
Venue: Conference KDD 2010
Year: 2010

This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his/her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8%, and inference quality of explicit interests by up to 101%.
Stemming in the Language Modeling Framework
Authors: James Allan, Giridhar Kumaran
Venue: Conference SIGIR 2003
Year: 2003

Stemming is the process of collapsing words into their morphological root. For example, the terms addicted, addicting, addictions, addictive, and addicts might be conflated to their stem, addict. Over the years, numerous studies [2, 3, 4] have considered stemming as an external process — either to be ignored or used as a pre-processing step. In this study, we try and provide a fresh perspective to stemming. We are motivated by the observation that stemming can be viewed as a form of smoothing, as a way of improving statistical estimates. This suggests that stemming could be directly incorporated into a language model, which is what we achieve in this paper. Detailed discussions are available in[1].
Language processing for arabic microblog retrieval
Authors: Kareem Darwish, Walid Magdy, Ahmed Mourad
Venue: Conference CIKM 2012
Year: 2012

The use of social media has profoundly affected social and political dynamics in the Arab world. In this paper, we explore the Arabic microblogs retrieval. We illustrate some of the challenges associated with Arabic microblog retrieval, which mainly stem from the use of different Arabic dialects that vary in lexical selection, morphology, and phonetics and lack orthographic and spelling conventions. We present some of the required processing for effective retrieval such as improved letter normalization, elongated word handling, stopword removal, and stemming
Time is of the essence: improving recency ranking using twitter data
Authors: Anlei Dong, Ruiqiang Zhang, Pranam Kolari, Jing Bai, Fernando Diaz 0001, Yi Chang 0001, Zhaohui Zheng 0001, Hongyuan Zha
Venue: Conference WWW 2010
Year: 2010

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.
Aspect and sentiment unification model for online review analysis
Authors: Yohan Jo, Alice Oh
Venue: Conference WSDM 2011
Year: 2011

User-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the problem of automatically discovering what aspects are evaluated in reviews and how sentiments for different aspects are expressed. We first propose Sentence-LDA (SLDA), a probabilistic generative model that assumes all words in a single sentence are generated from one aspect. We then extend SLDA to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects. ASUM discovers pairs of {aspect, sentiment} which we call senti-aspects. We applied SLDA and ASUM to reviews of electronic devices and restaurants. The results show that the aspects discovered by SLDA match evaluative details of the reviews, and the senti-aspects found by ASUM capture important aspects that are closely coupled with a sentiment. The results of sentiment classification show that ASUM outperforms other generative models and comes close to supervised classification methods. One important advantage of ASUM is that it does not require any sentiment labels of the reviews, which are often expensive to obtain.
Correlating financial time series with micro-blogging activity
Authors: Eduardo J. Ruiz, Vagelis Hristidis, Carlos Castillo, Aristides Gionis, Alejandro Jaimes
Venue: Conference WSDM 2012
Year: 2012

We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the micro-blogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties. We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However, by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.
High accuracy retrieval with multiple nested ranker
Authors: Irina Matveeva, Chris Burges, Timo Burkard, Andy Laucius, Leon Wong
Venue: Conference SIGIR 2006
Year: 2006

High precision at the top ranks has become a new focus of research in information retrieval. This paper presents the multiple nested ranker approach that improves the accuracy at the top ranks by iteratively re-ranking the top scoring documents. At each iteration, this approach uses the RankNet learning algorithm to re-rank a subset of the results. This splits the problem into smaller and easier tasks and generates a new distribution of the results to be learned by the algorithm. We evaluate this approach using different settings on a data set labeled with several degrees of relevance. We use the normalized discounted cumulative gain (NDCG) to measure the performance because it depends not only on the position but also on the relevance score of the document in the ranked list. Our experiments show that making the learning algorithm concentrate on the top scoring results improves precision at the top ten documents in terms of the NDCG score.
Mining Contentions from Discussions and Debates
Authors: Arjun Mukherjee, Bing Liu
Venue: Conference KDD 2012
Year: 2012

Social media has become a major source of information for many applications. Numerous techniques have been proposed to analyze network structures and text contents. In this paper, we focus on fine-grained mining of contentions in discussion/debate forums. Contentions are perhaps the most important feature of forums that discuss social, political and religious issues. Our goal is to discover contention and agreement indicator expressions, and contention points or topics both at the discussion collection level and also at each individual post level. To the best of our knowledge, limited work has been done on such detailed analysis. This paper proposes three models to solve the problem, which not only model both contention/agreement expressions and discussion topics, but also, more importantly, model the intrinsic nature of discussions/debates, i.e., interactions among discussants or debaters and topic sharing among posts through quoting and replying relations. Evaluation results using real-life discussion/debate posts from several domains demonstrate the effectiveness of the proposed models.
Location inference using microblog messages
Authors: Yohei Ikawa, Miki Enoki, Michiaki Tatsubori
Venue: Conference WWW 2012
Year: 2012

In order to sense and analyze disaster information from social media, microblogs as sources of social data have recently attracted attention. In this paper, we attempt to discover geolocation information from microblog messages to assess disasters. Since microblog services are more timely compared to other social media, understanding the geolocation information of each microblog message is useful for quickly responding to a sudden disasters. Some microblog services provide a function for adding geolocation information to messages from mobile device equipped with GPS detectors. However, few users use this function, so most messages do not have geolocation information. Therefore, we attempt to discover the location where a message was generated by using its textual content. The proposed method learns associations between a location and its relevant keywords from past messages, and guesses where a new message came from.
Video suggestion and discovery for youtube: taking random walks through the view graph
Authors: Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, Mohamed Aly
Venue: Conference WWW 2008
Year: 2008

The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the difficulty of searching videos, the size of the video repository also makes the discovery of new content a daunting task. In this paper, we present a novel method based upon the analysis of the entire user-video graph to provide personalized video suggestions for users. The resulting algorithm, termed Adsorption, provides a simple method to efficiently propagate preference information through a variety of graphs. We extensively test the results of the recommendations on a three month snapshot of live data from YouTube.
Codewebs: scalable homework search for massive open online programming courses
Authors: Andy Nguyen, Christopher Piech, Jonathan Huang, Leonidas J. Guibas
Venue: Conference WWW 2014
Year: 2014

Massive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale may cure the ``cost disease" of higher education. While scalable in many ways, providing feedback for homework submissions (particularly open-ended ones) remains a challenge in the online classroom. In courses where the student-teacher ratio can be ten thousand to one or worse, it is impossible for instructors to personally give feedback to students or to understand the multitude of student approaches and pitfalls. Organizing and making sense of massive collections of homework solutions is thus a critical web problem. Despite the challenges, the dense solution space sampling in highly structured homeworks for some MOOCs suggests an elegant solution to providing quality feedback to students on a massive scale.
One seed to find them all: Mining opinion features via association
Authors: Zhen Hai, Kuiyu Chang, Gao Cong
Venue: Conference CIKM 2012
Year: 2012

Feature-based opinion analysis has attracted extensive attention recently. Identifying features associated with opinions expressed in reviews is essential for fine-grained opinion mining. One approach is to exploit the dependency relations that occur naturally between features and opinion words, and among features (or opinion words) themselves. In this paper, we propose a generalized approach to opinion feature extraction by incorporating robust statistical association analysis in a bootstrapping framework. The new approach starts with a small set of feature seeds, on which it iteratively enlarges by mining feature-opinion, feature-feature, and opinion-opinion dependency relations. Two association model types, namely likelihood ratio tests (LRT) and latent semantic analysis (LSA), are proposed for computing the pair-wise associations between terms (features or opinions). We accordingly propose two robust bootstrapping approaches, LRTBOOT and LSABOOT, both of which need just a handful of initial feature seeds to bootstrap opinion feature extraction. We benchmarked LRTBOOT and LSABOOT against existing approaches on a large number of real-life reviews crawled from the cellphone and hotel domains. Experimental results using varying number of feature seeds show that the proposed association-based bootstrapping approach significantly outperforms the competitors. In fact, one seed feature is all that is needed for LRTBOOT to significantly outperform the other methods. This seed feature can simply be the domain feature, e.g., "cellphone" or "hotel". The consequence of our discovery is far reaching: starting with just one feature seed, typically just the domain concept word, LRTBOOT can automatically extract a large set of high-quality opinion features from the corpus without any supervision or labeled features. This means that the automatic creation of a set of domain features is no longer a pipe dream!
Opinion integration through semi-supervised topic modeling
Authors: Yue Lu, Chengxiang Zhai
Venue: Conference WWW 2008
Year: 2008

Web 2.0 technology has enabled more and more people to freely express their opinions on the Web, making the Web an extremely valuable source for mining user opinions about all kinds of topics. In this paper we study how to automatically integrate opinions expressed in a well-written expert review with lots of opinions scattering in various sources such as blogspaces and forums. We formally define this new integration problem and propose to use semi-supervised topic models to solve the problem in a principled way. Experiments on integrating opinions about two quite different topics (a product and a political figure) show that the proposed method is effective for both topics and can generate useful aligned integrated opinion summaries. The proposed method is quite general. It can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.
Rated aspect summarization of short comments
Authors: Yue Lu, ChengXiang Zhai, Neel Sundaresan
Venue: Conference WWW 2009
Year: 2009

Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summary'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how well human agree on such a summarization task. The proposed methods are quite general and can be used to generate rated aspect summary automatically given any collection of short comments each associated with an overall rating.
Ambiguous queries: test collections need more sense
Authors: Mark Sanderson
Venue: Conference SIGIR 2008
Year: 2008

Although there are many papers examining ambiguity in Information Retrieval, this paper shows that there is a whole class of ambiguous word that past research has barely explored. It is shown that the class is more ambiguous than other word types and is commonly used in queries. The lack of test collections containing ambiguous queries is highlighted and a method for creating collections from existing resources is described. Tests using the new collection show the impact of query ambiguity on an IR system: it is shown that conventional systems are incapable of dealing effectively with such queries and that current assumptions about how to improve search effectiveness do not hold when searching on this common query type.
Text segmentation via topic modeling: an analytical study
Authors: Hemant Misra, François Yvon, Joemon M. Jose, Olivier Cappé
Venue: Conference CIKM 2009
Year: 2009

In this paper, the task of text segmentation is approached from a topic modeling perspective. We investigate the use of latent Dirichlet allocation (LDA) topic model to segment a text into semantically coherent segments. A major benefit of the proposed approach is that along with the segment boundaries, it outputs the topic distribution associated with each segment. This information is of potential use in applications like segment retrieval and discourse analysis. The new approach outperforms a standard baseline method and yields significantly better performance than most of the available unsupervised methods on a benchmark dataset.
A practical web-based approach to generating topic hierarchy for text segments
Authors: Shui-Lung Chuang, Lee-Feng Chien
Venue: Conference CIKM 2004
Year: 2004

It is crucial in many information systems to organize short text segments, such as keywords in documents and queries from users, into a well-formed topic hierarchy. In this paper, we address the problem of generating topic hierarchies for diverse text segments with a general and practical approach that uses the Web as an additional knowledge source. Unlike long documents, short text segments typically do not contain enough information to extract reliable features. This work investigates the possibilities of using highly ranked search-result snippets to enrich the representation of text segments. A hierarchical clustering algorithm is then applied to create the hierarchical topic structure of text segments. Different from traditional clustering algorithms, which tend to produce cluster hierarchies with a very unnatural shape, the approach tries to produce a more natural and comprehensive hierarchy. Extensive experiments were conducted on different domains of text segments. The obtained results have shown the potential of the proposed approach, which is believed able to benefit many information systems.