OpenRaiser/Intern-Atlas huggingface dataset:
Inducing conceptual embedding spaces from Wikipedia
Authors: Gerard de Melo
Venue: Conference WWW 2017
Year: 2017

The word2vec word vector representations are one of the most well-known new semantic resources to appear in recent years. While large sets of pre-trained vectors are available, these focus on frequent words and multi-word expressions but lack sufficient coverage of named entities. Moreover, Google only released pre-trained vectors for English. In this paper, we explore an automatic expansion of Google's pre-trained vectors using Wikipedia, adding millions of concepts and named entities in over 270 languages. Our method enables all of these to reside in the same vector space, thus flexibly facilitating cross-lingual semantic applications.
The dual-sparse topic model: mining focused topics and focused terms in short text
Authors: Tianyi Lin, Wentao Tian, Qiaozhu Mei, Hong Cheng
Venue: Conference WWW 2014
Year: 2014

Topic modeling has been proved to be an effective method for exploratory text mining. It is a common assumption of most topic models that a document is generated from a mixture of topics. In real-world scenarios, individual documents usually concentrate on several salient topics instead of covering a wide variety of topics. A real topic also adopts a narrow range of terms instead of a wide coverage of the vocabulary. Understanding this sparsity of information is especially important for analyzing user-generated Web content and social media, which are featured as extremely short posts and condensed discussions. In this paper, we propose a dual-sparse topic model that addresses the sparsity in both the topic mixtures and the word usage. By applying a "Spike and Slab" prior to decouple the sparsity and smoothness of the document-topic and topic-word distributions, we allow individual documents to select a few focused topics and a topic to select focused terms, respectively. Experiments on different genres of large corpora demonstrate that the dual-sparse topic model outperforms both classical topic models and existing sparsity-enhanced topic models. This improvement is especially notable on collections of short documents.
Assessing the news landscape: A multi-module toolkit for evaluating the credibility of news
Authors: Benjamin D. Horne, William Dron, Sara Khedr, Sibel Adali
Venue: Conference WWW 2018
Year: 2018

Today, journalist, information analyst, and everyday news consumers are tasked with discerning and fact-checking the news. This task has became complex due to the ever-growing number of news sources and the mixed tactics of maliciously false sources. To mitigate these problems, we introduce the The News Landscape (NELA) Toolkit: an open source toolkit for the systematic exploration of the news landscape. NELA allows users to explore the credibility of news articles using well-studied content-based markers of reliability and bias, as well as, filter and sort through article predictions based on the user's own needs. In addition, NELA allows users to visualize the media landscape at different time slices using a variety of features computed at the source level. NELA is built with a modular, pipeline design, to allow researchers to add new tools to the toolkit with ease. Our demo is an early transition of automated news credibility research to assist human fact-checking efforts and increase the understanding of the news ecosystem as a whole.
Pairfac: Event analytics through discriminant tensor factorization
Authors: Xidao Wen, Yu-Ru Lin, Konstantinos Pelechrinis
Venue: Conference CIKM 2016
Year: 2016

The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this paper, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.
On participation in group chats on Twitter
Authors: Ceren Budak, Rakesh Agrawal
Venue: Conference WWW 2013
Year: 2013

The success of a group depends on continued participation of its members through time. We study the factors that affect continued user participation in the context of educational Twitter chats. To predict whether a user that attended her first session in a particular Twitter chat group will return to the group, we build 5F Model that captures five different factors: individual initiative, group characteristics, perceived receptivity, linguistic affinity and geographical proximity. Through statistical data analysis of thirty Twitter chats over a two year period as well as a survey study, our work provides many insights about group dynamics in Twitter chats. We show similarities between Twitter chats and traditional groups such as the importance of social inclusion and linguistic similarity while also identifying important distinctions such as the insignificance of geographical proximity. We also show that informational support is more important than emotional support in educational Twitter chats, but this does not reduce the sense of community as suggested in earlier studies.
Find the conversation killers: A predictive study of thread-ending posts
Authors: Yunhao Jiao, Cheng Li, Fei Wu, Qiaozhu Mei
Venue: Conference WWW 2018
Year: 2018

How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, civil, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposed model. For the widely concerned topic, our analysis also offers implications for how to improve the quality and user experience of online conversations, or how to engage users in a conversation with a chatbot.
Recognizing humor on twitter
Authors: Renxian Zhang, Naishi Liu
Venue: Conference CIKM 2014
Year: 2014

In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network. The central question of what makes a tweet (Twitter post) humorous drives us to design humor-related features, which are derived from influential humor theories, linguistic norms, and affective dimensions. Using machine learning techniques, we are able to recognize humorous tweets with high accuracy and F-measure. More importantly, we single out features that contribute to distinguishing non-humorous tweets from humorous tweets, and humorous tweets from other short humorous texts (non-tweets). This proves that humorous tweets possess discernible characteristics that are neither found in plain tweets nor in humorous non-tweets. We believe our novel findings will inform and inspire the burgeoning field of computational humor research in the social media.
Paraphrastic fusion for abstractive multi-sentence compression generation
Authors: Mir Tafseer Nayeem, Yllias Chali
Venue: Conference CIKM 2017
Year: 2017

This paper presents a first attempt towards finding an abstractive compression generation system for a set of related sentences which jointly models sentence fusion and paraphrasing using continuous vector representations. Our paraphrastic fusion system improves the informativity and the grammaticality of the generated sentences. Our system can be applied to various real world applications such as text simplification, microblog, opinion and newswire summarization. We conduct our experiments on human generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Our experiments demonstrate that our method brings significant improvements over the state of the art systems across different metrics.
Strategies for retrieving plagiarized documents
Authors: Benno Stein, Sven Meyer zu Eissen, Martin Potthast
Venue: Conference SIGIR 2007
Year: 2007

For the identification of plagiarized passages in large document collections we present retrieval strategies which rely on stochastic sampling and chunk indexes. Using the entire Wikipedia corpus we compile n-gram indexes and compare them to a new kind of fingerprint index in a plagiarism analysis use case. Our index provides an analysis speed-up by factor 1.5 and is an order of magnitude smaller, while being equivalent in terms of precision and recall.
Early exit optimizations for additive machine learned ranking systems
Authors: Berkant Barla Cambazoglu, Hugo Zaragoza, Olivier Chapelle, Jiang Chen, Ciya Liao, Zhaohui Zheng, Jon Degenhardt
Venue: Conference WSDM 2010
Year: 2010

Some commercial web search engines rely on sophisticated machine learning systems for ranking web documents. Due to very large collection sizes and tight constraints on query response times, online efficiency of these learning systems forms a bottleneck. An important problem in such systems is to speedup the ranking process without sacrificing much from the quality of results. In this paper, we propose optimization strategies that allow short-circuiting score computations in additive learning systems. The strategies are evaluated over a state-of-the-art machine learning system and a large, real-life query log, obtained from Yahoo!. By the proposed strategies, we are able to speedup the score computations by more than four times with almost no loss in result quality.
Contextual intent tracking for personal assistants
Authors: Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, Rui Zhang
Venue: Conference KDD 2016
Year: 2016

A new paradigm of recommendation is emerging in intelligent personal assistants such as Apple's Siri, Google Now, and Microsoft Cortana, which recommends "the right information at the right time" and proactively helps you "get things done". This type of recommendation requires precisely tracking users' contemporaneous intent, i.e., what type of information (e.g., weather, stock prices) users currently intend to know, and what tasks (e.g., playing music, getting taxis) they intend to do. Users' intent is closely related to context, which includes both external environments such as time and location, and users' internal activities that can be sensed by personal assistants. The relationship between context and intent exhibits complicated co-occurring and sequential correlation, and contextual signals are also heterogeneous and sparse, which makes modeling the context intent relationship a challenging task. To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent. The KP2 model utilizes collaborative capabilities among users, and learns for each user a personalized dynamic system that enables efficient nowcasting of users' intent. Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying large-scale proactive recommendation systems in personal assistants.
Crosslingual document embedding as reduced-rank ridge regression
Authors: Martin Josifoski, Ivan S. Paskov, Hristo S. Paskov, Martin Jaggi, Robert West
Venue: Conference WSDM 2019
Year: 2019

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. Moreover, since our algorithm uses the singular value decomposition as its core operation, it is highly scalable. Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task. Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.
Interactive search for image categories by mental matching
Authors: Marin Ferecatu, Donald Geman
Venue: Conference ICCV 2007
Year: 2007

Traditional image retrieval methods require a "query image" to initiate a search for members of an image category. However, when the image database is unstructured, and when the category is semantic and resides only in the mind of the user, there is no obvious way to begin (the "page zero " problem). We propose a new mathematical framework for relevance feedback based on mental matching and starting from a random sample of images. At each iteration the user declares which of several displayed images is closest to his category; performance is measured by the number of iterations necessary to display an instance. Our core contribution is a Bayesian formulation which scales to large databases with no semantic annotation. The two key components are a response model which accounts for the user's subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and a database with 20,000 images demonstrate the efficiency of the search process.
Decision trees with minimal costs
Authors: Charles X. Ling, Qiang Yang 0001, Jianning Wang, Shichao Zhang 0001
Venue: Conference ICML 2004
Year: 2004

We propose a simple, novel and yet effective method for building and testing decision trees that minimizes the sum of the misclassification and test costs. More specifically, we first put forward an original and simple splitting criterion for attribute selection in tree building. Our tree-building algorithm has many desirable properties for a cost-sensitive learning system that must account for both types of costs. Then, assuming that the test cases may have a large number of missing values, we design several intelligent test strategies that can suggest ways of obtaining the missing values at a cost in order to minimize the total cost. We experimentally compare these strategies and C4.5, and demonstrate that our new algorithms significantly outperform C4.5 and its variations. In addition, our algorithm's complexity is similar to that of C4.5, and is much lower than that of previous work. Our work is useful for many diagnostic tasks which must factor in the misclassification and test costs for obtaining missing information.
Table cell search for question answering
Authors: Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su, Xifeng Yan
Venue: Conference WWW 2016
Year: 2016

Tables are pervasive on the Web. Informative web tables range across a large variety of topics, which can naturally serve as a significant resource to satisfy user information needs. Driven by such observations, in this paper, we investigate an important yet largely under-addressed problem: Given millions of tables, how to precisely retrieve table cells to answer a user question. This work proposes a novel table cell search framework to attack this problem. We first formulate the concept of a relational chain which connects two cells in a table and represents the semantic relation between them. With the help of search engine snippets, our framework generates a set of relational chains pointing to potentially correct answer cells. We further employ deep neural networks to conduct more fine-grained inference on which relational chains best match the input question and finally extract the corresponding answer cells. Based on millions of tables crawled from the Web, we evaluate our framework in the open-domain question answering (QA) setting, using both the well-known WebQuestions dataset and user queries mined from Bing search engine logs. On WebQuestions, our framework is comparable to state-of-the-art QA systems based on knowledge bases (KBs), while on Bing queries, it outperforms other systems with a 56.7% relative gain. Moreover, when combined with results from our framework, KB-based QA performance can obtain a relative improvement of 28.1% to 66.7%, demonstrating that web tables supply rich knowledge that might not exist or is difficult to be identified in existing KBs.
How many results per page?: A Study of SERP Size, Search Behavior and User Experience
Authors: Diane Kelly, Leif Azzopardi
Venue: Conference SIGIR 2015
Year: 2015

The provision of \\ten blue links" has emerged as the standard\nfor the design of search engine result pages (SERPs).\nWhile numerous aspects of SERPs have been examined, little\nattention has been paid to the number of results displayed\nper page. This paper investigates the relationships among\nthe number of results shown on a SERP, search behavior\nand user experience. We performed a laboratory experiment\nwith 36 subjects, who were randomly assigned to use\none of three search interfaces that varied according to the\nnumber of results per SERP (three, six or ten). We found\nsubjects' click distributions differed significantly depending\non SERP size. We also found those who interacted with\nthree results per page viewed significantly more SERPs per\nquery; interestingly, the number of SERPs they viewed per\nquery corresponded to about 10 search results. Subjects who\ninteracted with ten results per page viewed and saved significantly more documents. They also reported the greatest\ndifficulty finding relevant documents, rated their skills the\nlowest and reported greater workload, even though these\ndifferences were not significant. This work shows that behavior\nchanges with SERP size, such that more time is spent\nfocused on earlier results when SERP size decreases.
Humor detection in product question answering systems
Authors: Yftah Ziser, Elad Kravi, David Carmel
Venue: Conference SIGIR 2020
Year: 2020

Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due to their unreasonable price, their peculiar functionality, or in cases that users emphasize their critical point-of-view through humor. Detecting humorous questions in such systems is important for sellers, to better understand user engagement with their products. It is also important to signal users about flippancy of humorous questions, and that answers for such questions should be taken with a grain of salt.
Analyzing and predicting news popularity in an instant messaging service
Authors: Mohammad Naseri, Hamed Zamani
Venue: Conference SIGIR 2019
Year: 2019

With widespread use of mobile devices, instant messaging (IM) services have recently attracted a great deal of attention by millions of users. This has motivated news agencies to share their contents via such platforms in addition to their websites and popular social media. As a result, thousands of users nowadays follow the news agencies through their verified channels in IM services. However, user interactions with such platforms is relatively unstudied. In this paper, we provide an initial study to analyze and predict news popularity in an instant messaging service. To this aim, we focus on Telegram, a popular IM service with 200 million monthly active users. We explore the differences between news popularity analysis in Telegram and typical social media, such as Twitter, and highlight its unique characteristics. We perform our analysis on the data we collected from four diverse news agencies. Following our analysis, we study the task of news popularity prediction in Telegram and show that the performance of the prediction models can be substantially improved by learning from the data of multiple news agencies using multi-task learning. To foster research in this area, we have made the collected data publicly available.
More than just words: Modeling non-textual characteristics of podcasts
Authors: Longqi Yang, Yu Wang, Drew Dunne, Michael Sobolev, Mor Naaman, Deborah Estrin
Venue: Conference WSDM 2019
Year: 2019

Recent years have witnessed the flourishing of podcasts, a unique type of audio medium. Prior work on podcast content modeling focused on analyzing Automatic Speech Recognition outputs, which ignored vocal, musical, and conversational properties (e.g., energy, humor, and creativity) that uniquely characterize this medium. In this paper, we present an Adversarial Learning-based Podcast Representation (ALPR) that captures non-textual aspects of podcasts. Through extensive experiments on a large-scale podcast dataset (88,728 episodes from 18,433 channels), we show that (1) ALPR significantly outperforms the state-of-the-art features developed for music and speech in predicting theseriousness andenergy of podcasts, and (2) incorporating ALPR significantly improves the performance of topic-based podcast-popularity prediction. Our experiments also reveal factors that correlate with podcast popularity.