The relative ineffectiveness of information retrieval systems is largely caused by the
inaccuracy with which a query formed by a few keywords models the actual user information …

Locations, eg, countries, states, cities, and point-of-interests, are central to news, emergency
events, and people's daily lives. Automatic identification of locations associated with or …

We propose a simple and effective re-ranking method for improving passage retrieval in
open question answering. The re-ranker re-scores retrieved passages with a zero-shot …

Users often fail to formulate their complex information needs in a single query. As a
consequence, they may need to scan multiple result pages or reformulate their queries …

In recent years, deep neural networks have led to exciting breakthroughs in speech
recognition, computer vision, and natural language processing (NLP) tasks. However, there …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

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 …

Methods of deep neural networks (DNNs) have recently demonstrated superior performance
on a number of natural language processing tasks. However, in most previous work, the …

Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …