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Gennady Pekhimenko, Dimitrios Lymberopoulos, Oriana Riva, Karin Strauss, and Doug Burger

Trending search topics cause unpredictable query load spikes that hurt the end-user search experience, particularly the mobile one, by introducing longer delays. To understand how trending search topics are formed and evolve over time, we analyze 21 million queries submitted during periods where popular events caused search query volume spikes. Based on our findings, we design and evaluate PocketTrend, a system that automatically detects trending topics in real time, identifies the search...

Publication details
Date: 1 May 2015
Type: Inproceeding
Ryen W. White, Matthew Richardson, and Wen-tau Yih

Search systems traditionally require searchers to formulate information needs as keywords rather than in a more natural form, such as questions. Recent studies have found that Web search engines are observing an increase in the fraction of queries phrased as natural language. As part of building better search engines, it is important to understand the nature and prevalence of these intentions, and the impact of this increase on search engine performance. In this work, we show that while 10.3% of queries...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He

Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Wen Hua, Zhongyuan Wang, Haixun Wang, Kai Zheng, and Xiaofang Zhou

Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, short texts usually do not contain suffi cient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, short texts are usually more ambiguous. We argue that knowledge is needed in order to better...

Publication details
Date: 1 April 2015
Type: Inproceeding
Emre Kıcıman

While today’s structured knowledge bases (e.g., Freebase) contain a sizable collection of information about entities, from celebrities and locations to concepts and common objects, there is a class of knowledge that has minimal coverage: actions. A large-scale knowledge base of actions would provide an opportunity for computing devices to aid and support people’s reasoning about their own actions and outcomes, leading to improved decision-making and goal achievement. In this short paper, we...

Publication details
Date: 23 March 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Publication details
Date: 1 February 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Xian-Sheng Hua and Jin Li

With the advances in distributed computation, machine learning and deep neural networks, we enter into an era that it is possible to build a real world image recognition system. There are three essential components to build a real-world image recognition system: 1) creating representative features, 2) de-signing powerful learning approaches, and 3) identifying massive training data. While extensive researches have been done on the first two aspects, much less attention has been paid on the third. In...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Nihar B. Shah and Dengyong Zhou

Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker abilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference problem. As far as we know, none of the objective functions in existing methods is convex. In machine learning and applied statistics, a convex function such as the objective function of...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Elad Yom-Tov, Diana Borsa, Andrew C Hayward, Rachel A McKendry, and Ingemar J Cox

Background: The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual’s risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors.

Objective: Here we report that Web...

Publication details
Date: 1 January 2015
Type: Article
Publisher: JMIR
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng

In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the...

Publication details
Date: 12 December 2014
Type: Inproceeding
Fang Wang, Zhongyuan Wang, Senzhang Wang, and Zhoujun Li

Keyphrase extraction is essential for many IR and NLP tasks. Existing methods usually use the phrases of the document separately without distinguishing the potential semantic correlations among them, or other statistical features from knowledge bases such as WordNet and Wikipedia. However, the mutual semantic information between phrases is also important, and exploiting their correlations may potentially help us more effectively extract the keyphrases. Generally, phrases in the title are more likely to...

Publication details
Date: 1 December 2014
Type: Inproceeding
Larry Heck and Hongzhao Huang

This paper presents an unsupervised neural knowledge graph embedding model and a coherence-based approach for semantic parsing of Twitter dialogs. The approach learns embeddings directly from knowledge graphs and scales to all of Wikipedia. Experiments show a 23.6% reduction in semanticparsing errors compared to the previously best reported results.

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng

We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a...

Publication details
Date: 1 December 2014
Type: Article
Publisher: arXiv
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng

We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a...

Publication details
Date: 1 December 2014
Type: Article
Publisher: arXiv
Michael J. Paul, Ryen W. White, and Eric Horvitz

We seek to understand the evolving needs of people who are faced with a life-changing medical diagnosis based on analyses of queries extracted from an anonymized search query log. Focusing on breast cancer, we manually tag a set of Web searchers as showing disruptive shifts in focus of attention and long-term patterns of search behavior consistent with the diagnosis and treatment of breast cancer. We build and apply probabilistic classifiers to detect these searchers from multiple sessions and to detect...

Publication details
Date: 15 November 2014
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2014-144
Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, and Krishnaram Kenthapadi

The rapid proliferation of hand-held devices has led to the development of rich, interactive and immersive applications, such as e-readers for electronic books. These applications motivate retrieval systems that can implicitly satisfy any information need of the reader by exploiting the context of the user’s interactions. Such retrieval systems differ from traditional search engines in that the queries constructed using the context are typically complex objects (including the document and its...

Publication details
Date: 4 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Sreenivas Gollapudi and Debmalya Panigrahi

where A key characteristic of a successful online market is the large specific participation of agents (producers and consumers) on both definition sides of the market. While there has been a long line of tion problems, impressive work on understanding such markets in terms of main revenue maximizing (also called max-sum) objectives, par- • ticularly in the context of allocating online impressions to interested advertisers, fairness considerations have surprisingly not received much attention in online...

Publication details
Date: 4 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Yuanhua Lv and ChengXiang Zhai

Pseudo-relevance feedback (PRF) has proven to be an effective strategy for improving retrieval accuracy. In this paper, we revisit a PRF method based on statistical language models, namely the divergence minimization model (DMM). DMM not only has apparently sound theoretical foundation, but also has been shown to satisfy most of the retrieval constraints. However, it turns out to perform surprisingly poorly in many previous experiments. We investigate the cause, and reveal that DMM inappropriately...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM
Emine Yilmaz, Manisha Verma, Nick Craswell, Filip Radlinski, and Peter Bailey

Relevance judgments sit at the core of test collection construction, and are assumed to model the utility of documents to real users. However, comparisons of judgments with signals of relevance obtained from real users, such as click counts and dwell time, have demonstrated a systematic mismatch.

In this paper, we study one important source of the mismatch between user data and relevance judgments: Those due to the high degree of effort required by users to identify and consume the information in...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil

In this paper, we propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents. In order to capture the rich contextual structures in a query or a document, we start with each word within a temporal context window in a word sequence to directly capture contextual features at the word n-gram level. Next, the salient word n-gram features in the word sequence are...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: CIKM
Danyel Fisher, Badrish Chandramouli, Robert DeLine, Jonathan Goldstein, Andrei Aron, Mike Barnett, John C. Platt, James F. Terwilliger, John Wernsing, danyelf badrishc, and rdeline jongold

Over the last two decades, data scientists performed increasingly sophisticated analyses on larger data sets, yet their tools and workflows remain low-level. A typical analysis involves different tools for different stages of the work, requiring file transfers and considerable care to keep everything organized. Temporal data adds additional complexity: users typically must write queries offline before porting them to production systems. To address these problems, this paper introduces Tempe, a web...

Publication details
Date: 1 November 2014
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2014-148
Zhaohui Wu, Yuanhua Lv, and Ariel Fuxman

When consuming content, users typically encounter entities that they are not familiar with. A common scenario is when users want to find information about entities directly within the content they are consuming. For example, when reading the book "Adventures of Huckleberry Finn", a user may lose track of the character Mary Jane and want to find some paragraph in the book that gives relevant information about her. The way this is achieved today is by invoking the ubiquitous Find function ("Ctrl-F")....

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM
Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi

Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query.

Perhaps surprisingly, despite QAC being widely used, users’ interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: CIKM
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen

We examine the embedding approach to reason new relational facts from a large-scale knowledge graph and a text corpus. We propose a novel method of jointly embedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the knowledge graph and the concurrences of words in the text corpus. Entity names and Wikipedia anchors are utilized to align the embeddings of entities and words in the same space. Large scale experiments...

Publication details
Date: 1 October 2014
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
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