OpenRaiser/Intern-Atlas huggingface dataset:
News to go: hierarchical text summarization for mobile devices
Authors: Jahna Otterbacher, Dragomir R. Radev, Omer Kareem
Venue: Conference SIGIR 2006
Year: 2006

We present an evaluation of a novel hierarchical text summarization method that allows users to view summaries of Web documents from small, mobile devices. Unlike previous approaches, ours does not require the documents to be in HTML since it infers a hierarchical structure automatically. Currently, the method is used to summarize news articles sent to a Web mail account in plain text format. Subjects used a Web-enabled mobile phone emulator to access the account's inbox and view the summarized news articles. They then used the summaries to complete several information-seeking tasks, which involved answering factual questions about the stories. In comparing the hierarchical text summary setting to that in which subjects were given the full text articles, there was no significant difference in task accuracy or the time taken to complete the task. However, in the hierarchical summarization setting, the number of bytes transferred per user request is less than half that of the full text case. Finally, in comparing the new method to three other summarization methods, subjects achieved significantly better accuracy on the tasks when using hierarchical summaries.
Trains of Thought: Generating Information Maps
Authors: Dafna Shahaf, Carlos Guestrin, Eric Horvitz
Venue: Conference WWW 2012
Year: 2012

When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge into a single coherent picture. Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. Our proposed algorithm generates a concise structured set of documents maximizing coverage of salient pieces of information. Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development. We first formalize characteristics of good maps and formulate their construction as an optimization problem. Then we provide efficient methods with theoretical guarantees for generating maps. Finally, we integrate user interaction into our framework, allowing users to alter the maps to better reflect their interests. Pilot user studies with a real-world dataset demonstrate that the method is able to produce maps which help users acquire knowledge efficiently.
Fractal summarization: summarization based on fractal theory
Authors: Christopher C. Yang, Fu Lee Wang
Venue: Conference SIGIR 2003
Year: 2003

In this paper, we introduce the fractal summarization model based on the fractal theory. In fractal summarization, the important information is captured from the source text by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the original is produced iteratively using the contractive transformation in the fractal theory. User evaluation has shown that fractal summarization outperforms traditional summarization.
Autopedia: Automatic domain-independent wikipedia article generation
Authors: Conglei Yao, Xu Jia, Sicong Shou, Shicong Feng, Feng Zhou, Hongyan Liu
Venue: Conference WWW 2011
Year: 2011

This paper proposes a general framework, named Autopedia, to generate high-quality wikipedia articles for given concepts in any domains, by automatically selecting the best wikipedia template consisting the sub-topics to organize the article for the input concept. Experimental results on 4,526 concepts validate the effectiveness of Autopedia, and the wikipedia template selection approach which takes into account both the template quality and the semantic relatedness between the input concept and its sibling concepts, performs the best.
Learning query intent from regularized click graphs
Authors: Xiao Li, Ye-Yi Wang, Alex Acero
Venue: Conference SIGIR 2008
Year: 2008

This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.
Clustering product features for opinion mining
Authors: Zhongwu Zhai, Bing Liu, Hua Xu, Peifa Jia
Venue: Conference WSDM 2011
Year: 2011

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin.
A general regression technique for learning transductions
Authors: Corinna Cortes, Mehryar Mohri, Jason Weston
Venue: Conference ICML 2005
Year: 2005

The problem of learning a transduction, that is a string-to-string mapping, is a common problem arising in natural language processing and computational biology. Previous methods proposed for learning such mappings are based on classification techniques. This paper presents a new and general regression technique for learning transductions and reports the results of experiments showing its effectiveness. Our transduction learning consists of two phases: the estimation of a set of regression coefficients and the computation of the pre-image corresponding to this set of coefficients. A novel and conceptually cleaner formulation of kernel dependency estimation provides a simple framework for estimating the regression coefficients, and an efficient algorithm for computing the pre-image from the regression coefficients extends the applicability of kernel dependency estimation to output sequences. We report the results of a series of experiments illustrating the application of our regression technique for learning transductions.
Route Kernels for Trees
Authors: Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti
Venue: Conference ICML 2009
Year: 2009

Almost all tree kernels proposed in the literature match substructures without taking into account their relative positioning with respect to one another. In this paper, we propose a novel family of kernels which explicitly focus on this type of information. Specifically, after defining a family of tree kernels based on routes between nodes, we present an efficient implementation for a member of this family. Experimental results on four different datasets show that our method is able to reach state of the art performances, obtaining in some cases performances better than computationally more demanding tree kernels.
A longitudinal study of real-time search assistance adoption
Authors: Peter G. Anick, Raj Gopal Prasad Kantamneni
Venue: Conference SIGIR 2008
Year: 2008

We present findings from a log based study designed to track the adoption of features of a new real-time query refinement interface deployed on the Yahoo search engine. Several trends from the first four months are noted and discussed.
Finding question-answer pairs from online forums
Authors: Gao Cong, Long Wang, Chin-Yew Lin, Young-In Song, Yueheng Sun
Venue: Conference SIGIR 2008
Year: 2008

Online forums contain a huge amount of valuable user generated content. In this paper we address the problem of extracting question-answer pairs from forums. Question-answer pairs extracted from forums can be used to help Question Answering services (e.g. Yahoo! Answers) among other applications. We propose a sequential patterns based classification method to detect questions in a forum thread, and a graph based propagation method to detect answers for questions in the same thread. Experimental results show that our techniques are very promising.
Two-part segmentation of text documents
Authors: P. Deepak, Karthik Visweswariah, Nirmalie Wiratunga, Sadiq Sani
Venue: Conference CIKM 2012
Year: 2012

We consider the problem of segmenting text documents that have a two-part structure such as a problem part and a solution part. Documents of this genre include incident reports that typically involve description of events relating to a problem followed by those pertaining to the solution that was tried. Segmenting such documents into the component two parts would render them usable in knowledge reuse frameworks such as Case-Based Reasoning. This segmentation problem presents a hard case for traditional text segmentation due to the lexical inter-relatedness of the segments. We develop a two-part segmentation technique that can harness a corpus of similar documents to model the behavior of the two segments and their inter-relatedness using language models and translation models respectively. In particular, we use separate language models for the problem and solution segment types, whereas the inter-relatedness between segment types is modeled using an IBM Model 1 translation model. We model documents as being generated starting from the problem part that comprises of words sampled from the problem language model, followed by the solution part whose words are sampled either from the solution language model or from a translation model conditioned on the words already chosen in the problem part. We show, through an extensive set of experiments on real-world data, that our approach outperforms the state-of-the-art text segmentation algorithms in the accuracy of segmentation, and that such improved accuracy translates well to improved usability in Case-based Reasoning systems. We also analyze the robustness of our technique to varying amounts and types of noise and empirically illustrate that our technique is quite noise tolerant, and degrades gracefully with increasing amounts of noise.
Kernel methods, syntax and semantics for relational text categorization
Authors: Alessandro Moschitti
Venue: Conference CIKM 2008
Year: 2008

Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntac-tic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately solve such retrieval tasks. However, when the latter involve the computation of relational se-mantics between text fragments simple statistical models may re-sult ineffective. In this paper, we show that syntactic and semantic structures can be used to greatly improve complex categorization tasks such as determining if an answer correctly responds to a ques-tion. Given the high complexity of representing semantic/syntactic structures in learning algorithms, we applied kernel methods along with Support Vector Machines to better exploit the needed rela-tional information. Our experiments on answer classification on Web and TREC data show that our models greatly improve on bag-of-words.
Structural relationships for large-scale learning of answer re-ranking
Authors: Aliaksei Severyn, Alessandro Moschitti
Venue: Conference SIGIR 2012
Year: 2012

Supervised learning applied to answer re-ranking can highly improve on the overall accuracy of question answering (QA) systems. The key aspect is that the relationships and properties of the question/answer pair composed of a question and the supporting passage of an answer candidate, can be efficiently compared with those captured by the learnt model.
Supervised learning from multiple experts: whom to trust when everyone lies a bit
Authors: Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy
Venue: Conference ICML 2009
Year: 2009

We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.
A web-based kernel function for measuring the similarity of short text snippets
Authors: Mehran Sahami, Timothy D. Heilman
Venue: Conference WWW 2006
Year: 2006

Determining the similarity of short text snippets, such as search queries, works poorly with traditional document similarity measures (e.g., cosine), since there are often few, if any, terms in common between two short text snippets. We address this problem by introducing a novel method for measuring the similarity between short text snippets (even those without any overlapping terms) by leveraging web search results to provide greater context for the short texts. In this paper, we define such a similarity kernel function, mathematically analyze some of its properties, and provide examples of its efficacy. We also show the use of this kernel function in a large-scale system for suggesting related queries to search engine users.
Copulas for information retrieval
Authors: Carsten Eickhoff, Arjen P. de Vries, Kevyn Collins-Thompson
Venue: Conference SIGIR 2013
Year: 2013

In many domains of information retrieval, system estimates of document relevance are based on multidimensional quality criteria that have to be accommodated in a unidimensional result ranking. Current solutions to this challenge are often inconsistent with the formal probabilistic framework in which constituent scores were estimated, or use sophisticated learning methods that make it difficult for humans to understand the origin of the final ranking. To address these issues, we introduce the use of copulas, a powerful statistical framework for modeling complex multi-dimensional dependencies, to information retrieval tasks. We provide a formal background to copulas and demonstrate their effectiveness on standard IR tasks such as combining multidimensional relevance estimates and fusion of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to significant performance improvements on several tasks, as evaluated on large-scale standard corpora, compared to their non-copula counterparts. We also investigate criteria for understanding the likely effect of using copula models in a given retrieval scenario.
Detecting opinion leaders and trends in online social networks
Authors: Freimut Bodendorf, Carolin Kaiser
Venue: Conference CIKM 2009
Year: 2009

Today, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. A new approach based on text mining and social network analysis is presented which allows detecting opinion leaders and opinion trends. This allows getting a better understanding of the opinion formation. The overall concept is presented and illustrated by an example.
Latent aspect rating analysis on review text data: a rating regression approach
Authors: Hongning Wang, Yue Lu, Chengxiang Zhai
Venue: Conference KDD 2010
Year: 2010

In this paper, we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis (LARA), which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity. We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way. Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.
Generating event storylines from microblogs
Authors: Chen Lin, Chun Lin, Jingxuan Li, Dingding Wang, Yang Chen, Tao Li
Venue: Conference CIKM 2012
Year: 2012

Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on real-time events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.
A study of retrospective and on-line event detection
Authors: Yiming Yang, Thomas Pierce, Jaime G. Carbonell
Venue: Conference SIGIR 1998
Year: 1998

This paper investigates the use and extension of text retrieval and clustering techniques for event detection. The task is to automatically detect novel events from a temporally-ordered stream of news stories, either retrospectively or as the stories arrive. We applied hierarchical and non-hierarchical document clustering algorithms to a corpus of 15,836 stories, focusing on the exploitation of both content and temporal information. We found the resulting cluster hierarchies highly informative for retrospective detection of previously unidentified events, effectively supporting both query-free and query-driven retrieval. We also found that temporal distribution patterns of document clusters provide useful information for improvement in both retrospective detection and on-line detection of novel events. In an evaluation using manually labelled events to judge the system-detected events, we obtained a result of 82% in the Fl measure for retrospective detection, and a Fl value of 42% for on-line detection.
Learning hierarchical similarity metrics
Authors: Nakul Verma, Dhruv Mahajan 0001, Sundararajan Sellamanickam, Vinod Nair
Venue: Conference CVPR 2012
Year: 2012

Categories in multi-class data are often part of an underlying semantic taxonomy. Recent work in object classification has found interesting ways to use this taxonomy structure to develop better recognition algorithms. Here we propose a novel framework to learn similarity metrics using the class taxonomy. We show that a nearest neighbor classifier using the learned metrics gets improved performance over the best discriminative methods. Moreover, by incorporating the taxonomy, our learned metrics can also help in some taxonomy specific applications. We show that the metrics can help determine the correct placement of a new category that was not part of the original taxonomy, and can provide effective classification amongst categories local to specific subtrees of the taxonomy.
Detecting offensive tweets via topical feature discovery over a large scale twitter corpus
Authors: Guang Xiang, Bin Fan, Ling Wang, Jason I. Hong, Carolyn P. Rosé
Venue: Conference CIKM 2012
Year: 2012

In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.
Using graded-relevance metrics for evaluating community qa answer selection
Authors: Tetsuya Sakai, Daisuke Ishikawa, Noriko Kando, Yohei Seki, Kazuko Kuriyama, Chin-Yew Lin
Venue: Conference WSDM 2011
Year: 2011

Community Question Answering (CQA) sites such as Yahoo! Answers have emerged as rich knowledge resources for information seekers. However, answers posted to CQA sites can be irrelevant, incomplete, redundant, incorrect, biased, ill-formed or even abusive. Hence, automatic selection of “good ” answers for a given posted question is a practical research problem that will help us manage the quality of accumulated knowledge. One way to evaluate answer selection systems for CQA would be to use the Best Answers (BAs) that are readily available from the CQA sites. However, BAs may be biased, and even if they are not, there may be other good answers besides BAs. To remedy these two problems, we propose system evaluation methods that involve multiple answer assessors and graded-relevance information retrieval metrics. Our main findings from experiments using the NTCIR-8 CQA task data are that, using our evaluation methods, (a) we can detect many substantial differences between systems that would have been overlooked by BA-based evaluation; and (b) we can better identify hard questions (i.e. those that are handled poorly by many systems and therefore require focussed investigation) compared to BAbased evaluation. We therefore argue that our approach is useful for building effective CQA answer selection systems despite the cost of manual answer assessments.