OpenRaiser/Intern-Atlas huggingface dataset:
Mediarank: Computational ranking of online news sources
Authors: Junting Ye, Steven Skiena
Venue: Conference KDD 2019
Year: 2019

In the recent political climate, the topic of news quality has drawn attention both from the public and the academic communities. The growing distrust of traditional news media makes it harder to find a common base of accepted truth. In this work, we design and build MediaRank (urlwww.media-rank.com ), a fully automated system to rank over 50,000 online news sources around the world. MediaRank collects and analyzes one million news webpages and two million related tweets everyday. We base our algorithmic analysis on four properties journalists have established to be associated with reporting quality: peer reputation, reporting bias/breadth, bottomline financial pressure, and popularity. Our major contributions of this paper include: (i) Open, interpretable quality rankings for over 50,000 of the world's major news sources. Our rankings are validated against 35 published news rankings, including French, German, Russian, and Spanish language sources. MediaRank scores correlate positively with 34 of 35 of these expert rankings. (ii) New computational methods for measuring influence and bottomline pressure. To the best of our knowledge, we are the first to study the large-scale news reporting citation graph in-depth. We also propose new ways to measure the aggressiveness of advertisements and identify social bots, establishing a connection between both types of bad behavior. (iii) Analyzing the effect of media source bias and significance. We prove that news sources cite others despite different political views in accord with quality measures. However, in four English-speaking countries (US, UK, Canada, and Australia), the highest ranking sources all disproportionately favor left-wing parties, even when the majority of news sources exhibited conservative slants.
Causal relation of queries from temporal logs
Authors: Yizhou Sun, Kunqing Xie, Ning Liu, Shuicheng Yan, Benyu Zhang, Zheng Chen
Venue: Conference WWW 2007
Year: 2007

In this paper, we study a new problem of mining causal relation of queries in search engine query logs. Causal relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the causal relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the causal relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the causal relation of queries is detected effectively.
Dgl-ke: Training knowledge graph embeddings at scale
Authors: Da Zheng 0004, Xiang Song 0003, Chao Ma 0025, Zeyuan Tan, Zihao Ye 0001, Jin Dong, Hao Xiong, Zheng Zhang 0001, George Karypis
Venue: Conference SIGIR 2020
Year: 2020

Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever growing size of knowledge graphs requires computationally efficient algorithms capable of scaling to graphs with millions of nodes and billions of edges. This paper presents DGL-KE, an open-source package to efficiently compute knowledge graph embeddings. DGL-KE introduces various novel optimizations that accelerate training on knowledge graphs with millions of nodes and billions of edges using multi-processing, multi-GPU, and distributed parallelism. These optimizations are designed to increase data locality, reduce communication overhead, overlap computations with memory accesses, and achieve high operation efficiency. Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine. These results represent a 2× ~ 5× speedup over the best competing approaches. DGL-KE is available on https://github.com/awslabs/dgl-ke.
MEmbER: Max-margin based embeddings for entity retrieval
Authors: Shoaib Jameel, Zied Bouraoui, Steven Schockaert
Venue: Conference SIGIR 2017
Year: 2017

We propose a new class of methods for learning vector space embeddings of entities. While most existing methods focus on modelling similarity, our primary aim is to learn embeddings that are interpretable, in the sense that query terms have a direct geometric representation in the vector space. Intuitively, we want all entities that have some property (i.e. for which a given term is relevant) to be located in some well-defined region of the space. This is achieved by imposing max-margin constraints that are derived from a bag-of-words representation of the entities. The resulting vector spaces provide us with a natural vehicle for identifying entities that have a given property (or ranking them according to how much they have the property), and conversely, to describe what a given set of entities have in common. As we show in our experiments, our models lead to a substantially better performance in a range of entity-oriented search tasks, such as list completion and entity ranking.
Learning latent vector spaces for product search
Authors: Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Venue: Conference CIKM 2016
Year: 2016

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
Authors: Li Zhang, Shuo Zhang, Krisztian Balog
Venue: Conference SIGIR 2019
Year: 2019

Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row population, column population, and table retrieval, by incorporating them into existing retrieval models as additional semantic similarity signals. Evaluation results show that table embeddings can significantly improve upon the performance of state-of-the-art baselines.
A hybrid retrieval-generation neural conversation model
Authors: Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu
Venue: Conference CIKM 2019
Year: 2019

Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.
A compare-aggregate model with latent clustering for answer selection
Authors: Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung
Venue: Conference CIKM 2019
Year: 2019

In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing. First, we explore the effect of additional information by adopting a pretrained language model to compute the vector representation of the input text and by applying transfer learning from a large-scale corpus. Second, we enhance the compare-aggregate model by proposing a novel latent clustering method to compute additional information within the target corpus and by changing the objective function from listwise to pointwise. To evaluate the performance of the proposed approaches, experiments are performed with the WikiQA and TREC-QA datasets. The empirical results demonstrate the superiority of our proposed approach, which achieve state-of-the-art performance for both datasets.
Real-time rumor debunking on twitter
Authors: Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, Sameena Shah
Venue: Conference CIKM 2015
Year: 2015

In this paper, we propose the first real time rumor debunking algorithm for Twitter. We use cues from 'wisdom of the crowds', that is, the aggregate 'common sense' and investigative journalism of Twitter users. We concentrate on identification of a rumor as an event that may comprise of one or more conflicting microblogs. We continue monitoring the rumor event and generate real time updates dynamically based on any additional information received. We show using real streaming data that it is possible, using our approach, to debunk rumors accurately and efficiently, often much faster than manual verification by professionals.
Targeted disambiguation of ad-hoc, homogeneous sets of named entities
Authors: Chi Wang, Kaushik Chakrabarti, Tao Cheng, Surajit Chaudhuri
Venue: Conference WWW 2012
Year: 2012

In many entity extraction applications, the entities to be recognized are constrained to be from a list of “target entities”. In many cases, these target entities are (i) ad-hoc, i.e., do not exist in a knowledge base and (ii) homogeneous (e.g., all the entities are IT companies). We study the following novel disambiguation problem in this unique setting: given the candidate mentions of all the target entities, determine which ones are true mentions of a target entity. Prior techniques only consider target entities present in a knowledge base and/or having a rich set of attributes. In this paper, we develop novel techniques that require no knowledge about the entities excepttheir names. Ourmain insight is to leverage the homogeneity constraint and disambiguate the candidate mentions collectively across all documents. We propose a graph-based model, called MentionRank, for that purpose. Furthermore, if additional knowledge is available for some or all of the entities, our model can leverage it to further improve quality. Our experiments demonstrate the effectiveness of our model. To the best of our knowledge, this is the first work on targeted entity disambiguation for ad-hoc entities.
Multi-document topic segmentation
Authors: Minwoo Jeong, Ivan Titov
Venue: Conference CIKM 2010
Year: 2010

Multiple documents describing the same or closely related sets of events are common and often easy to obtain: for example, consider document clusters on a news aggregator site or multiple reviews of the same product or service. Even though each such document discusses a similar set of topics, they provide alternative views or complimentary information on each of these topics. We argue that revealing hidden relations by jointly segmenting the documents, or, equivalently, predicting links between topically related segments in different documents would help to visualize documents of interest and construct friendlier user interfaces. In this paper, we refer to this problem as multi-document topic segmentation. We propose an unsupervised Bayesian model for the considered problem that models both shared and document-specific topics, and utilizes Dirichlet process priors to determine the effective number of topics. We show that topic segmentation can be inferred efficiently using a simple split-merge sampling algorithm. The resulting method outperforms baseline models on four datasets for multi-document topic segmentation.
Generic topic segmentation of document texts
Authors: Marie-Francine Moens, Rik De Busser
Venue: Conference SIGIR 2001
Year: 2001

Topic segmentation is an important initial step in many text-based tasks. A hierarchical representation of a texts topics is useful in retrieval and allows judging relevancy at different levels of detail. This short paper describes research on generic algorithms for topic detection and segmentation that are applicable on texts of heterogeneous types and domains.
Style mining of electronic messages for multiple authorship discrimination: First results
Authors: Shlomo Argamon, Marin Saric, Sterling Stuart Stein
Venue: Conference KDD 2003
Year: 2003

This paper considers the use of computational stylistics for performing authorship attribution of electronic messages, addressing categorization problems with as many as 20 different classes (authors). Effective stylistic characterization of text is potentially useful for a variety of tasks, as language style contains cues regarding the authorship, purpose, and mood of the text, all of which would be useful adjuncts to information retrieval or knowledge-management tasks. We focus here on the problem of determining the author of an anonymous message, based only on the message text. Several multiclass variants of the Winnow algorithm were applied to a vector representation of the message texts to learn models for discriminating different authors. We present results comparing the classification accuracy of the different approaches. The results show that stylistic models can be accurately learned to determine an author's identity.
Personalized semantic word vectors
Authors: Javid Ebrahimi, Dejing Dou
Venue: Conference CIKM 2016
Year: 2016

Distributed word representations are able to capture syntactic and semantic regularities in text. In this paper, we present a word representation scheme that incorporates authorship information. While maintaining similarity among related words in the induced distributed space, our word vectors can be effectively used for some text classification tasks too. We build on a log-bilinear document model (lbDm), which extracts document features, and word vectors based on word co-occurrence counts. First, we propose a log-bilinear author model (lbAm), which contains an additional author matrix. We show that by directly learning author feature vectors, as opposed to document vectors, we can learn better word representations for the authorship attribution task. Furthermore, authorship information has been found to be useful for sentiment classification. We enrich the author model with a sentiment tensor, and demonstrate the effectiveness of this hybrid model (lbHm) through our experiments on a movie review-classification dataset.
Leveraging Emotional Signals for Credibility Detection
Authors: Anastasia Giachanou, Paolo Rosso, Fabio Crestani
Venue: Conference SIGIR 2019
Year: 2019

The spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.
MAF: A general matching and alignment framework for multimodal named entity recognition
Authors: Bo Xu, Shizhou Huang, Chaofeng Sha, Hongya Wang
Venue: Conference WSDM 2022
Year: 2022

In this paper, we study multimodal named entity recognition in social media posts. Existing works mainly focus on using a cross-modal attention mechanism to combine text representation with image representation. However, they still suffer from two weaknesses: (1) the current methods are based on a strong assumption that each text and its accompanying image are matched, and the image can be used to help identify named entities in the text. However, this assumption is not always true in real scenarios, and the strong assumption may reduce the recognition effect of theMNER model; (2) the current methods fail to construct a consistent representation to bridge the semantic gap between two modalities, which prevents the model from establishing a good connection between the text and image. To address these issues, we propose a general matching and alignment framework (MAF) for multimodal named entity recognition in social media posts. Specifically, to solve the first issue, we propose a novel cross-modal matching (CM) module to calculate the similarity score between text and image, and use the score to determine the proportion of visual information that should be retained. To solve the second issue, we propose a novel cross-modal alignment (CA) module to make the representations of the two modalities more consistent. We conduct extensive experiments, ablation studies, and case studies to demonstrate the effectiveness and efficiency of our method.The source code of this paper can be found in https://github.com/xubodhu/MAF.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Authors: Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha
Venue: Conference KDD 2020
Year: 2020

Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes. To secure government revenue without interrupting legitimate trade flows, customs administrations around the world strive to develop ways to detect illicit trades. This paper proposes DATE, a model of Dual-task Attentive Tree-aware Embedding, to classify and rank illegal trade flows that contribute the most to the overall customs revenue when caught. The strength of DATE comes from combining a tree-based model for interpretability and transaction-level embeddings with dual attention mechanisms. To accurately identify illicit transactions and predict tax revenue, DATE learns simultaneously from illicitness and surtax of each transaction. With a five-year amount of customs import data with a test illicit ratio of 2.24%, DATE shows a remarkable precision of 92.7% on illegal cases and a recall of 49.3% on revenue after inspecting only 1% of all trade flows. We also discuss issues on deploying DATE in Nigeria Customs Service, in collaboration with the World Customs Organization.
Model ensemble for click prediction in bing search ads
Authors: Xiaoliang Ling, Weiwei Deng, Chen Gu, Hucheng Zhou, Cui Li, Feng Sun
Venue: Conference WWW 2017
Year: 2017

Accurate estimation of the click-through rate (CTR) in sponsored ads significantly impacts the user search experience and businesses' revenue, even 0.1% of accuracy improvement would yield greater earnings in the hundreds of millions of dollars. CTR prediction is generally formulated as a supervised classification problem. In this paper, we share our experience and learning on model ensemble design and our innovation. Specifically, we present 8 ensemble methods and evaluate them on our production data. Boosting neural networks with gradient boosting decision trees turns out to be the best. With larger training data, there is a nearly 0.9% AUC improvement in offline testing and significant click yield gains in online traffic. In addition, we share our experience and learning on improving the quality of training.
The fact: Taming latent factor models for explainability with factorization trees
Authors: Yiyi Tao, Yiling Jia, Nan Wang, Hongning Wang
Venue: Conference SIGIR 2019
Year: 2019

Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regularization imposed by the tree structure. As a result, we are able to track the creation of latent profiles by looking into the path of each factor on regression trees, which thus serves as an explanation for the resulting recommendations. Extensive experiments on two large collections of Amazon and Yelp reviews demonstrate the advantage of our model over several competitive baseline algorithms. Besides, our extensive user study also confirms the practical value of explainable recommendations generated by our model.
Assessing the credibility of claims on the web
Authors: Kashyap Popat
Venue: Conference WWW 2017
Year: 2017

In my doctoral research, I plan to address the problem of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting. Automatic credibility assessment is a complex task depending upon many factors. To start with, we propose three factors which can help in assessing the credibility of textual claims: (i) the reliability of the web sources talking about the claim, (ii) the language style of the articles reporting the claim and, (iii) their stance (i.e., support or refute) towards the claim. In addition, we also focus on extracting user-interpretable explanations as evidence supporting the verdict of the assessment.
Improving Recommendation Lists Through Topic Diversification
Authors: Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, Georg Lausen
Venue: Conference WWW 2005
Year: 2005

In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.