OpenRaiser/Intern-Atlas huggingface dataset:
Health Misinformation in Search and Social Media
Authors: Amira Ghenai
Venue: SIGIR
Year: 2017

People regularly use web search and social media to investigate health related issues. This type of Internet data might contain misinformation i.e. incorrect information which contradicts current established medical understanding. If people are influenced by the presented misinformation in these sources, they can make harmful decisions about their health. My research goal is to investigate the effect of Internet data on people's health. Working with my colleagues on this topic, our current findings suggest that there is a potential for people to be harmed by search engine results. Furthermore, we successfully built a high precision approach to track misinformation in social media. In this paper, I briefly discuss my current work including background key references. Thereafter, I propose a research plan to understand possible mechanisms of misinformation's effect on people and its possible impacts on public health. Later, I will explain the suggested research methodology to achieve the research plan.
A Collection for Detecting Triggers of Sentiment Spikes
Authors: Anastasia Giachanou, Ida Mele, Fabio Crestani
Venue: SIGIR
Year: 2017

The advent of social media has given the opportunity to users to publicly express and share their opinion about any topic. Public opinion is very important for the interested entities that can leverage such information in the process of making decisions. In addition, identifying sentiment changes and the likely causes that have triggered them allows interested parties to adjust their strategies and attract more positive sentiment. With the aim to facilitate research on this problem, we describe a collection of tweets that can be used for detecting and ranking the likely triggers of sentiment spikes towards different entities. To build the collection, we first group tweets by topic which are then manually annotated according to sentiment polarity and strength. We believe that this collection can be useful for further research on detecting sentiment change triggers, sentiment analysis and sentiment prediction.
Intent-Aware Semantic Query Annotation
Authors: Rafael Glater, Rodrygo L. T. Santos, Nivio Ziviani
Venue: SIGIR
Year: 2017

Query understanding is a challenging task primarily due to the inherent ambiguity of natural language. A common strategy for improving the understanding of natural language queries is to annotate them with semantic information mined from a knowledge base. Nevertheless, queries with different intents may arguably benefit from specialized annotation strategies. For instance, some queries could be effectively annotated with a single entity or an entity attribute, others could be better represented by a list of entities of a single type or by entities of multiple distinct types, and others may be simply ambiguous. In this paper, we propose a framework for learning semantic query annotations suitable to the target intent of each individual query. Thorough experiments on a publicly available benchmark show that our proposed approach can significantly improve state-of-the-art intent-agnostic approaches based on Markov random fields and learning to rank. Our results further demonstrate the consistent effectiveness of our approach for queries of various target intents, lengths, and difficulty levels, as well as its robustness to noise in intent detection.
BitFunnel: Revisiting Signatures for Search
Authors: Bob Goodwin, Michael Hopcroft, Dan Luu, Alex Clemmer, Mihaela Curmei, Sameh Elnikety, Yuxiong He
Venue: SIGIR
Year: 2017

Since the mid-90s there has been a widely-held belief that signature files are inferior to inverted files for text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based on an inverted index. The driving factor behind the shift away from the inverted index was operational cost savings. This paper describes algorithmic innovations and changes in the cloud computing landscape that led us to reconsider and eventually field a technology that was once considered unusable. The BitFunnel algorithm directly addresses four fundamental limitations in bit-sliced block signatures. At the same time, our mapping of the algorithm onto a cluster offers opportunities to avoid other costs associated with signatures. We show these innovations yield a significant efficiency gain versus classic bit-sliced signatures and then compare BitFunnel with Partitioned Elias-Fano Indexes, MG4J, and Lucene.
Automatic and Semi-Automatic Document Selection for Technology-Assisted Review
Authors: Maura R. Grossman, Gordon V. Cormack, Adam Roegiest
Venue: SIGIR
Year: 2017

Abstract In the TREC Total Recall Track (2015-2016), participating teams could employ either fully automatic or human-assisted ("semi-automatic") methods to select documents for relevance assessment by a simulated human reviewer. According to the TREC 2016 evaluation, the fully automatic baseline method achieved a recall-precision breakeven ("R-precision") score of 0.71, while the two semi-automatic efforts achieved scores of 0.67 and 0.51. In this work, we investigate the extent to which the observed effectiveness of the different methods may be confounded by chance, by inconsistent adherence to the Track guidelines, by selection bias in the evaluation method, or by discordant relevance assessments. We find no evidence that any of these factors could yield relative effectiveness scores inconsistent with the official TREC 2016 ranking.
Structuring the Unstructured: From Startup to Making Sense of eBay's Huge eCommerce Inventory
Authors: Ido Guy, Kira Radinsky
Venue: SIGIR
Year: 2017

Electronic commerce continues to gain popularity in recent years. On eBay, one of the largest on-line marketplaces in the world, millions of new listings (items) are submitted by a variety of sellers every day. This renders a rich diverse inventory characterized by a particularly long tail. In addition, many items in the inventory lack basic structured information, such as product identifiers, brand, category, and other properties, due to sellers' tendency to input unstructured information only, namely title and description. Such inventory therefore requires a handful of large-scale solutions to assist in organizing the data and gaining business insights. In 2016, eBay acquired SalesPredict to help structure its unstructured data. In this proposed presentation, we will share the story of a research startup from its inception until its acquisition and integration as eBay's data science team. We will review the numerous challenges from research and engineering perspectives of a startup and the principal challenges the eBay data science organization deals with today. These include the identification of duplicate, similar, and related products; the extraction of name-value attributes from item titles and descriptions; the matching of items entered by sellers to catalog products; the ranking of item titles based on their likelihood to serve as "good" product titles; and the creation of "browse node" pages to address complex search queries from potential buyers. We will describe how the eBay data science team approaches these challenges and some of the solutions already launched to production. These solutions involve the use of large-scale machine learning, information retrieval, and natural language processing techniques, and should therefore be of interest to the SIGIR audience at large.
A Large-Scale Query Spelling Correction Corpus
Authors: Matthias Hagen, Martin Potthast, Marcel Gohsen, Anja Rathgeber, Benno Stein
Venue: SIGIR
Year: 2017

We present a new large-scale collection of 54,772 queries with manually annotated spelling corrections. For 9,170 of the queries (16.74%), spelling variants that are different to the original query are proposed. With its size, our new corpus is an order of magnitude larger than other publicly available query spelling corpora. In addition to releasing the new large-scale corpus, we also provide an implementation of the winner of the Microsoft Speller Challenge from~2011 and compare it on the different publicly available corpora to spelling corrections mined from Google and Bing. This way, we also shed some light on the spelling correction performance of state-of-the-art commercial search systems.
DBpedia-Entity v2: A Test Collection for Entity Search
Authors: Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, Jamie Callan
Venue: SIGIR
Year: 2017

The DBpedia-entity collection has been used as a standard test collection for entity search in recent years. We develop and release a new version of this test collection, DBpedia-Entity v2, which uses a more recent DBpedia dump and a unified candidate result pool from the same set of retrieval models. Relevance judgments are also collected in a uniform way, using the same group of crowdsourcing workers, following the same assessment guidelines. The result is an up-to-date and consistent test collection.To facilitate further research, we also provide details about the pre-processing and indexing steps, and include baseline results from both classical and recently developed entity search methods.
MAPping the probability of start-up success
Authors: David Hawking
Venue: SIGIR
Year: 2017

Twenty years ago, I fell into the trap of believing that good performance on TREC Ad Hoc could be turned into commercial success via an enterprise search (ES) start-up. Although we achieved a measure of success, the reality was very different from my expectations. It turned out that end-users cared about search relevance only to the extent of reaching for buckets of vitriol when search failed to find what they wanted, and people making purchasing decisions were more interested in other things: what repositories can be included in the search, how responsive search is to updates, what security models are provided, the appearance of the search pages, and achieving internal business goals -- even at the expense of end-user needs. Organizations often purchase search technology as part of another system, such as a content management system, or a records management system, leaving other repositories unsearchable or incompatibly searchable. Causes of enterprise search failure include dimensions studied in IR but an ES system often fails because of the way it is configured, or because it fails to cover information resources that end-users need. The dream of a comprehensive, highly relevant, fully secure single search interface to all of an organization's information is very rarely achieved. There is a huge market potential for a killer ES product but the biggest challenges in exploiting it lie outside the scope of IR research. I look forward to sharing lessons learned from attempting to commercialize IR technology, and new perspectives from my current employment at Bing.
Exploring the Query Halo Effect in Site Search: Leading People to Longer Queries
Authors: Djoerd Hiemstra, Claudia Hauff, Leif Azzopardi
Venue: SIGIR
Year: 2017

People tend to type short queries, however, the belief is that longer queries are more effective. Consequently, a number of attempts have been made to encourage and motivate people to enter longer queries. While most have failed, a recent attempt - conducted in a laboratory setup - in which the query box has a halo or glow effect, that changes as the query becomes longer, has been shown to increase query length by one term, on average. In this paper, we test whether a similar increase is observed when the same component is deployed in a production system for site search and used by real end users. To this end, we conducted two separate experiments, where the rate at which the color changes in the halo were varied. In both experiments users were assigned to one of two conditions: halo and no-halo. The experiments were ran over a fifty day period with 3,506 unique users submitting over six thousand queries. In both experiments, however, we observed no significant difference in query length. We also did not find longer queries to result in greater retrieval performance. While, we did not reproduce the previous findings, our results indicate that the query halo effect appears to be sensitive to performance and task, limiting its applicability to other contexts.
An Accurate, Efficient, and Scalable Approach to Channel Matching in Smart TVs
Authors: Jiwon Hong, Sang-Wook Kim, Mina Rho, YoonHee Choi, Yoonsik Tak
Venue: SIGIR
Year: 2017

Smart TVs are rapidly replacing conventional TVs. In a number of countries, set-top boxes (STB) are widely used to relay TV channels to smart TVs. In such cases, smart TVs cannot identify which TV channel they are receiving. This situation makes it challenging for smart TVs to provide their users with a variety of personalized services, such as context-aware services and recommendation services. In this paper, we introduce our TV channel matching system that resolves such problems. We propose strategies for scaling-out the matching system and improving its accuracy.
Mailbox-Based vs. Log-Based Query Completion for Mail Search
Authors: Michal Horovitz, Liane Lewin-Eytan, Alex Libov, Yoelle Maarek, Ariel Raviv
Venue: SIGIR
Year: 2017

Recent research studies on mail search have shown that the longer the query, the better the quality of results, yet a majority of mail queries remain very short and searchers struggle with formulating queries. A known mechanism to assist users in this task is query auto-completion, which has been highly successful in Web search, where it leverages huge logs of queries issued by hundreds of millions of users. This approach cannot be applied directly to mail search as personal query logs are small, mailboxes are not shared and other users' queries are not necessarily generalizable to all. We therefore propose here to leverage the mailbox content in order to generate suggestions, taking advantage of mail-specific features. We then compare this approach to a recent study that augments an individual user's mail search history with query logs from "similar users'', where the similarity is driven by demographics. Finally we show how combining both types of approaches allows for better suggestions quality but also increases the chance that the desired message be retrieved. We validate our claims via a manual qualitative evaluation and large scale quantitative experiments conducted on the query log of Yahoo Mail.
Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval
Authors: Fei Huang, Yong Cheng, Cheng Jin, Yuejie Zhang, Tao Zhang
Venue: SIGIR
Year: 2017

Fine-grained Sketch-based Image Retrieval (Fine-grained SBIR), which uses hand-drawn sketches to search the target object images, has been an emerging topic over the last few years. The difficulties of this task not only come from the ambiguous and abstract characteristics of sketches with less useful information, but also the cross-modal gap at both visual and semantic level. However, images on the web are always exhibited with multimodal contents. In this paper, we consider Fine-grained SBIR as a cross-modal retrieval problem and propose a deep multimodal embedding model that exploits all the beneficial multimodal information sources in sketches and images. In our experiment with large quantity of public data, we show that the proposed method outperforms the state-of-the-art methods for Fine-grained SBIR.
A Poisson Regression Method for Top-N Recommendation
Authors: Jiajin Huang, Jian Wang, Ning Zhong
Venue: SIGIR
Year: 2017

Top-N recommendation tasks aim to solve the information overload problem for users in the information age. As a user's decision may be affected by correlations among items, we incorporate such correlations with the user and item latent factors to propose a Poisson-regression-based method for top-N recommendation tasks. By placing priori knowledge and using a sparse structure assumption, this method learns the latent factors and the structure of the item-item correlation matrix through the alternating direction method of multipliers (ADMM). The preliminary experimental results on two real-world datasets show the improved performance of our approach.
Predicting Which Topics You Will Join in the Future on Social Media
Authors: Haoran Huang, Qi Zhang, Jindou Wu, Xuanjing Huang
Venue: SIGIR
Year: 2017

Every day, social media users send millions of microblogs on every imaginable topics. If we could predict which topics a user will join in the future, it would be easy to determine what topics will become popular and what kinds of users a topic may attract. It also can be of great interest for many applications. In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. We introduce a novel deep convolutional neural network with external neural memory and attention mechanism to perform this problem. User's posting history and topics were modeled with an external neural memory architecture. The convolutional neural network based matching methods were used to construct the relations between users and topics. Final decisions were made based on these matching results. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter. The experimental results demonstrated that the proposed method could perform significantly better than other methods. Comparing to the state-of-the-art deep neural networks, our approach achieves a relative improvement of 18.2\% in F1-score and 28.9\% in MAP@10.
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Authors: Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke
Venue: SIGIR
Year: 2017

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135× more data and 10× more topics without sacricing model quality.
Timestamping Entities using Contextual Information
Authors: Adam Jatowt, Daisuke Kawai, Katsumi Tanaka
Venue: SIGIR
Year: 2017

Wikipedia is the result of collaborative effort aiming to represent human knowledge and to make it accessible to the public. Many Wikipedia articles however lack key metadata information. For example, relatively large number of people described in Wikipedia have no information on their birth and death dates. We propose in this paper to estimate entity's lifetimes using link structure in Wikipedia focusing on person entities. Our approach is based on propagating temporal information over links between Wikipedia articles.
First International Workshop on Conversational Approaches to Information Retrieval (CAIR'17)
Authors: Hideo Joho, Lawrence Cavedon, Jaime Arguello, Milad Shokouhi, Filip Radlinski
Venue: SIGIR
Year: 2017

Recent advances in commercial conversational services that allow naturally spoken and typed interaction, particularly for well-formulated questions and commands, have increased the need for more human-centric interactions in information retrieval. The First International Workshop on Conversational Approaches to Information Retrieval (CAIR`17) brings together academic and industrial researchers to create a forum for research on conversational approaches to search. A specific focus is on techniques that support complex and multi-turn user-machine dialogues for information access and retrieval, and multi-model interfaces for interacting with such systems. We invite submissions addressing all modalities of conversation, including speech-based, text-based, and multimodal interaction. We also welcome studies of human-human interaction (e.g., collaborative search) that can inform the design of conversational search applications, and work on evaluation of conversational approaches.
Naver Search: Deep Learning Powered Search Portal for Intelligent Information Provision
Authors: Inho Kang
Venue: SIGIR
Year: 2017

Naver has been the most popular search engine for over a decade in South Korea. As a search portal, Naver aims to match a user's search intentions to the information from the web pages and databases, and to connect users based on shared interests to provide the best way to find the information. Over the past decade, Naver has been trying to better understand Korean users, queries, and web pages for PC and mobile search. In 2002, Naver introduced Knowledge-IN, which was the forerunner of community Question Answering to find out the need of users and topic experts. Users can ask their specific inquiry to appropriate topic experts in their search results. In addition to PC and mobile, Naver is trying to enable a user to access the relevant information using any other device or interface. In detecting common interest groups and good creators, Naver adds device and interface factors. Not only the contents, but also the delivery media types are important in satisfying users on various devices. Deep learning (DL) based methods have tremendous progress in image and text classification. With DL based methods, not only queries, and text documents, but also images, videos, live-streams, locations, etc. are classified and linked to detect common interest groups, and select and rank good creators and good delivery types in each group. With DL, Naver seeks to provide search results that meet user needs more precisely while learning and improving on the fly. In this talk, I'll cover some efforts and challenges in understanding and satisfying users on various devices.
LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency
Authors: Sosuke Kato, Riku Togashi, Hideyuki Maeda, Sumio Fujita, Tetsuya Sakai
Venue: SIGIR
Year: 2017

Recent advances in neural networks, along with the growth of rich and diverse community question answering (cQA) data, have enabled researchers to construct robust open-domain question answering (QA) systems. It is often claimed that such state-of-the-art QA systems far outperform traditional IR baselines such as BM25. However, most such studies rely on relatively small data sets, e.g., those extracted from the old TREC QA tracks. Given massive training data plus a separate corpus of Q&A pairs as the target knowledge source, how well would such a system really perform? How fast would it respond? In this demonstration, we provide the attendees of SIGIR 2017 an opportunity to experience a live comparison of two open-domain QA systems, one based on a long short-term memory (LSTM) architecture with over 11 million Yahoo! Chiebukuro (i.e., Japanese Yahoo! Answers) questions and over 27.4 million answers for training, and the other based on BM25. Both systems use the same Q&A knowledge source for answer retrieval. Our core demonstration system is a pair of Japanese monolingual QA systems, but we leverage machine translation for letting the SIGIR attendees enter English questions and compare the Japanese responses from the two systems after translating them into English.