OpenRaiser/Intern-Atlas huggingface dataset:
The Web Changes Everything: Understanding the dynamics of web content
Authors: Eytan Adar, Jaime Teevan, Susan T. Dumais, Jonathan L. Elsas
Venue: Conference WSDM 2009
Year: 2009

The Web is a dynamic, ever changing collection of information. This paper explores changes in Web content by analyzing a crawl of 55,000 Web pages, selected to represent different user visitation patterns. Although change over long intervals has been explored on random (and potentially unvisited) samples of Web pages, little is known about the nature of finer grained changes to pages that are actively consumed by users, such as those in our sample. We describe algorithms, analyses, and models for characterizing changes in Web content, focusing on both time (by using hourly and sub-hourly crawls) and structure (by looking at page-, DOM-, and term-level changes). Change rates are higher in our behavior-based sample than found in previous work on randomly sampled pages, with a large portion of pages changing more than hourly. Detailed content and structure analyses identify stable and dynamic content within each page. The understanding of Web change we develop in this paper has implications for tools designed to help people interact with dynamic Web content, such as search engines, advertising, and Web browsers.
Identifying content for planned events across social media sites
Authors: Hila Becker, Dan Iter, Mor Naaman, Luis Gravano
Venue: Conference WSDM 2012
Year: 2012

User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and analyze their effectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Authors: Stefan Siersdorfer, Sergiu Chelaru, Wolfgang Nejdl, José San Pedro
Venue: Conference WWW 2010
Year: 2010

An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
Twevent: Segment-based event detection from tweets
Authors: Chenliang Li, Aixin Sun, Anwitaman Datta
Venue: Conference CIKM 2012
Year: 2012

Event detection from tweets is an important task to understand the current events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for well written documents (e.g. news articles) cannot be directly adopted. In this paper, we propose a segment-based event detection system for tweets, called Twevent. Twevent first detects bursty tweet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tweet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tweets containing the segment published within that time window. The similarity between a pair of bursty segments is computed using their associated tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events. We evaluate Twevent and compare it with the state-of-the-art method using 4.3 million tweets published by Singapore-based users in June 2010. In our experiments, Twevent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Twevent can be easily interpreted with little background knowledge because of the newsworthy segments. We also show that Twevent is efficient and scalable, leading to a desirable solution for event detection from tweets.
Parametric and non-parametric user-aware sentiment topic models
Authors: Zaihan Yang, Alexander Kotov, Aravind Mohan, Shiyong Lu
Venue: Conference SIGIR 2015
Year: 2015

The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.
An unsupervised topic segmentation model incorporating word order
Authors: Shoaib Jameel, Wai Lam
Venue: Conference SIGIR 2013
Year: 2013

We present a new unsupervised topic discovery model for a collection of text documents. In contrast to the majority of the state-of-the-art topic models, our model does not break the document's structure such as paragraphs and sentences. In addition, it preserves word order in the document. As a result, it can generate two levels of topics of different granularity, namely, segment-topics and word-topics. In addition, it can generate n-gram words in each topic. We also develop an approximate inference scheme using Gibbs sampling method. We conduct extensive experiments using publicly available data from different collections and show that our model improves the quality of several text mining tasks such as the ability to support fine grained topics with n-gram words in the correlation graph, the ability to segment a document into topically coherent sections, document classification, and document likelihood estimation.
Multi-source deep learning for information trustworthiness estimation
Authors: Liang Ge, Jing Gao, Xiaoyi Li, Aidong Zhang
Venue: Conference KDD 2013
Year: 2013

In recent years, information trustworthiness has become a serious issue when user-generated contents prevail in our information world. In this paper, we investigate the important problem of estimating information trustworthiness from the perspective of correlating and comparing multiple data sources. To a certain extent, the consistency degree is an indicator of information reliability--Information unanimously agreed by all the sources is more likely to be reliable. Based on this principle, we develop an effective computational approach to identify consistent information from multiple data sources. Particularly, we analyze vast amounts of information collected from multiple review platforms (multiple sources) in which people can rate and review the items they have purchased. The major challenge is that different platforms attract diverse sets of users, and thus information cannot be compared directly at the surface. However, latent reasons hidden in user ratings are mostly shared by multiple sources, and thus inconsistency about an item only appears when some source provides ratings deviating from the common latent reasons. Therefore, we propose a novel two-step procedure to calculate information consistency degrees for a set of items which are rated by multiple sets of users on different platforms. We first build a Multi-Source Deep Belief Network (MSDBN) to identify the common reasons hidden in multi-source rating data, and then calculate a consistency score for each item by comparing individual sources with the reconstructed data derived from the latent reasons. We conduct experiments on real user ratings collected from Orbitz, Priceline and TripAdvisor on all the hotels in Las Vegas and New York City. Experimental results demonstrate that the proposed approach successfully finds the hotels that receive inconsistent, and possibly unreliable, ratings.
From truth discovery to trustworthy opinion discovery: An uncertainty-aware quantitative modeling approach
Authors: Mengting Wan, Xiangyu Chen, Lance M. Kaplan, Jiawei Han, Jing Gao, Bo Zhao
Venue: Conference KDD 2016
Year: 2016

In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.
Hybrid memnet for extractive summarization
Authors: Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma
Venue: Conference CIKM 2017
Year: 2017

Extractive text summarization has been an extensive research problem in the field of natural language understanding. While the conventional approaches rely mostly on manually compiled features to generate the summary, few attempts have been made in developing data-driven systems for extractive summarization. To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task. The network learns the continuous unified representation of a document before generating its summary. It jointly captures local and global sentential information along with the notion of summary worthy sentences. Experimental results on two different corpora confirm that our model shows significant performance gains compared with the state-of-the-art baselines.
Visualizing timelines: Evolutionary summarization via iterative reinforcement between text and image streams
Authors: Rui Yan, Xiaojun Wan, Mirella Lapata, Wayne Xin Zhao, Pu-Jen Cheng, Xiaoming Li
Venue: Conference CIKM 2012
Year: 2012

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators.
Linking named entities in tweets with knowledge base via user interest modeling
Authors: Wei Shen, Jianyong Wang, Ping Luo, Min Wang
Venue: Conference KDD 2013
Year: 2013

Twitter has become an increasingly important source of information, with more than 400 million tweets posted per day. The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base is called tweet entity linking. This task is of practical importance and can facilitate many different tasks, such as personalized recommendation and user interest discovery. The tweet entity linking task is challenging due to the noisy, short, and informal nature of tweets. Previous methods focus on linking entities in Web documents, and largely rely on the context around the entity mention and the topical coherence between entities in the document. However, these methods cannot be effectively applied to the tweet entity linking task due to the insufficient context information contained in a tweet. In this paper, we propose KAURI, a graph-based framework to collectively link all the named entity mentions in all tweets posted by a user via modeling the user's topics of interest. Our assumption is that each user has an underlying topic interest distribution over various named entities. KAURI integrates the intra-tweet local information with the inter-tweet user interest information into a unified graph-based framework. We extensively evaluated the performance of KAURI over manually annotated tweet corpus, and the experimental results show that KAURI significantly outperforms the baseline methods in terms of accuracy, and KAURI is efficient and scales well to tweet stream.
Unsupervised query segmentation using only query logs
Authors: Nikita Mishra, Rishiraj Saha Roy, Niloy Ganguly, Srivatsan Laxman, Monojit Choudhury
Venue: Conference WWW 2011
Year: 2011

We introduce an unsupervised query segmentation scheme that uses query logs as the only resource and can effectively capture the structural units in queries. We believe that Web search queries have a unique syntactic structure which is distinct from that of English or a bag-of-words model. The segments discovered by our scheme help understand this underlying grammatical structure. We apply a statistical model based on Hoeffding's Inequality to mine significant word n-grams from queries and subsequently use them for segmenting the queries. Evaluation against manually segmented queries shows that this technique can detect rare units that are missed by our Pointwise Mutual Information (PMI) baseline.
Cataloguing treatments discussed and used in online autism communities
Authors: Shaodian Zhang, Tian Kang, Lin Qiu, Weinan Zhang, Yong Yu, Noémie Elhadad
Venue: Conference WWW 2017
Year: 2017

A large number of patients discuss treatments in online health communities (OHCs). One research question of interest to health researchers is whether treatments being discussed in OHCs are eventually used by community members in their real lives. In this paper, we rely on machine learning methods to automatically identify attributions of mentions of treatments from an online autism community. The context of our work is online autism communities, where parents exchange support for the care of their children with autism spectrum disorder. Our methods are able to distinguish discussions of treatments that are associated with patients, caregivers, and others, as well as identify whether a treatment is actually taken. We investigate treatments that are not just discussed but also used by patients according to two types of content analysis, cross-sectional and longitudinal. The treatments identified through our content analysis help create a catalogue of real-world treatments. This study results lay the foundation for future research to compare real-world drug usage with established clinical guidelines.
Seisa: set expansion by iterative similarity aggregation
Authors: Yeye He, Dong Xin
Venue: Conference WWW 2011
Year: 2011

In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.
Detecting candidate named entities in search queries
Authors: Areej Alasiry, Mark Levene, Alexandra Poulovassilis
Venue: Conference SIGIR 2012
Year: 2012

The information extraction task of Named Entities Recognition (NER) has been recently applied to search engine queries, in order to better understand their semantics. Here we concentrate on the task prior to the classification of the named entities (NEs) into a set of categories, which is the problem of detecting candidate NEs via the subtask of query segmentation.We present a novel method for detecting candidate NEs using grammar annotation and query segmentation with the aid of top-n snippets from search engine results and a web n-gram model, to accurately identify NE boundaries. The proposed method addresses the problem of accurately setting boundaries of NEs and the detection of multiple NEs in queries.
Query to knowledge: Unsupervised entity extraction from shopping queries using adaptor grammars
Authors: Ke Zhai, Zornitsa Kozareva, Yuening Hu, Qi Li, Weiwei Guo
Venue: Conference SIGIR 2016
Year: 2016

Web search queries provide a surprisingly large amount of information, which can be potentially organized and converted into a knowledgebase. In this paper, we focus on the problem of automatically identifying brand and product entities from a large collection of web queries in online shopping domain. We propose an unsupervised approach based on adaptor grammars that does not require any human annotation efforts nor rely on any external resources. To reduce the noise and normalize the query patterns, we introduce a query standardization step, which groups multiple search patterns and word orderings together into their most frequent ones. We present three different sets of grammar rules used to infer query structures and extract brand and product entities. To give an objective assessment of the performance of our approach, we conduct experiments on a large collection of online shopping queries and intrinsically evaluate the knowledgebase generated by our method qualitatively and quantitatively. In addition, we also evaluate our framework on extrinsic tasks on query tagging and chunking. Our empirical studies show that the knowledgebase discovered by our approach is highly accurate, has good coverage and significantly improves the performance on the external tasks.
Retrieval of relevant opinion sentences for new products
Authors: Dae Hoon Park, Hyun Duk Kim, ChengXiang Zhai, Lifan Guo
Venue: Conference SIGIR 2015
Year: 2015

With the rapid development of Internet and E-commerce, abundant product reviews have been written by consumers who bought the products. These reviews are very useful for consumers to optimize their purchasing decisions. However, since the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel problem of retrieving relevant opinion sentences from the reviews of other products using specifications of a new or unpopular product as query. Our key idea is to leverage product specifications to assess product similarity between the query product and other products and extract relevant opinion sentences from the similar products where a consumer may find useful discussions. Then, we provide ranked opinion sentences for the query product that has no user-generated reviews. We first propose a popular summarization method and its modified version to solve the problem. Then, we propose our novel probabilistic methods. Experiment results show that the proposed methods can effectively retrieve useful opinion sentences for products that have no reviews.
NIFify: Towards Better Quality Entity Linking Datasets
Authors: Henry Rosales-Méndez, Aidan Hogan, Barbara Poblete
Venue: Conference WWW 2019
Year: 2019

The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with a corresponding unambiguous entry in a Knowledge Base. The evaluation of EL systems relies on the comparison of their results against gold standards. A common format used to represent gold standard datasets is the NLP Interchange Format (NIF), which uses RDF as a data model. However, creating gold standard datasets for EL is a time-consuming and error-prone process. In this paper we propose a tool called NIFify to help manually generate, curate, visualize and validate EL annotations; the resulting tool is useful, for example, in the creation of gold standard datasets. NIFify also serves as a benchmark tool that enables the assessment of EL results. Using the validation features of NIFify, we further explore the quality of popular EL gold standards.