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When it comes to emerging technologies, older adults are often those who can greatly benefit from the advancements but are vastly under-represented in research and designs. This study presents preliminary findings of older adults' search behavior with a ...

One of the central problems of artificial intelligence is machine perception, i.e., the ability to understand the visual world based on input from sensors such as cameras. In this talk, I will present recent progress of my team in this direction. I will ...

In this talk, I survey a small, non-random sample of research projects in information access carried out as part of the Thomson Reuters family of companies over the course of a 10+-year period. I analyse into how these projects are similar and different ...

Non-factoid question answering in the legal domain must provide legally correct, jurisdictionally relevant, and conversationally responsive answers to user-entered questions. We present work done on a QA system that is entirely based on IR and NLP, and ...

DSSM-like models showed good results in retrieval of short documents that semantically match the query. However, these models require large collections of click-through data that are not available in some domains. On the other hand, the recent advances ...

The most commonly used active learning criterion is uncertainty sampling, where a supervised model is used to predict uncertain samples that should be labeled next by a human annotator. When using active learning, two problems are encountered: First, ...

Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not ...

Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be ...

We describe a human-in-the loop system - AgentBuddy, that is helping Intuit improve the quality of search it offers to its internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time ...

We present a non-factoid QA system that provides legally accurate, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain. This commercially available system is entirely based on NLP and IR, and ...

Quantification (also known as "supervised prevalence estimation" [2], or "class prior estimation" [7]) is the task of estimating, given a set σ of unlabelled items and a set of classes C = c1, . . . , c |C| , the relative frequency (or "prevalence") p(...

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent ...

Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational ...

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of ...

The central idea of this paper is to gain a deeper understanding of song lyrics computationally. We focus on two aspects: style and biases of song lyrics. All prior works to understand these two aspects are limited to manual analysis of a small corpus ...

In this paper we propose the task of numeral attachment to detect the attached target of a numeral. Compared with other kinds of named entities, numerals provide richer and more crucial information in some domains. Fine-grained understanding of the ...

In this paper we propose a novel reinforcement learning based model for named entity recognition (NER), referred to as MM-NER. Inspired by the methodology of the AlphaGo Zero, MM-NER formalizes the problem of named entity recognition with a Monte-Carlo ...

Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend ...

Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR ...

Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. ...