Cross-Platform Question Answering in Social Networking Services

The last two decades have made the Internet a major source for knowledge seeking. Several platforms have been developed to find answers to one's questions such as search engines and online encyclopedias. The wide adoption of social networking services has pushed the possibilities even further by giving people the opportunity to stimulate the generation of answers that are not already present on the Internet. Some of these social media services are primarily community question answering (CQA) sites, while the others have a more general audience but can also be used to ask and answer questions. The choice of a particular platform (e.g., a CQA site, a microblogging service, or a search engine) by some user depends on several factors such as awareness of available resources and expectations from different platforms, and thus will sometimes be suboptimal. Hence, we introduce \emph{cross-platform question answering}, a framework that aims to improve our ability to satisfy complex information needs by returning answers from different platforms, including those where the question has not been originally asked. We propose to build this core capability by defining a general architecture for designing and implementing real-time services for answering naturally occurring questions. This architecture consists of four key components: (1) real-time detection of questions, (2) a set of platforms from which answers can be returned, (3) question processing by the selected answering systems, which optionally involves question transformation when questions are answered by services that enforce differing conventions from the original source, and (4) answer presentation, including ranking, merging, and deciding whether to return the answer. We demonstrate the feasibility of this general architecture by instantiating a restricted development version in which we collect the questions from one CQA website, one microblogging service or directly from the asker, and find answers from among some subset of those CQA and microblogging services. To enable the integration of new answering platforms in our architecture, we introduce a framework for automatic evaluation of their effectiveness

Question Answering over Curated and Open Web Sources

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants

A comparative approach to Question Answering Systems

In this paper I will analyze three different algorithms and approaches to implement Question Answering Systems (QA-Systems). I will analyze the efficiency, strengths, and weaknesses of multiple algorithms by explaining them in detail and comparing them with each other. The overarching aim of this thesis is to explore ideas that can be used to create a truly open context QA-System. Open context QA-Systems remain an open problem. The various algorithms and approaches presented in this work will be focused on complex questions. Complex questions are usually verbose and the context of the question is equally important to answer the query as is the question itself. Such questions represent an interesting problem in the field because they can be answered and written in a number of distinct ways. Also, the answer structure is not always the same and the QA-System needs to compensate for this. The analysis of complex questions differ between contexts. Contexts are the topic to which a complex question belongs, e.g. Biology, literature, etc… The analysis of the answer also differs according to the corpus used. Corpus is a set of documents, belonging to a specific context, where we can find the answer to a specified question. I will start by explaining various algorithms and approaches. I will then analyze its different parts. Finally, I will present some ideas on how to implement QA-Systems

Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION

Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models. Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time. We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used. Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one