241 citations · 686 across the 25 of their papers we have counts for
28 papers
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain
Andrew Yates, Michael Unterkalmsteiner
Domain-specific synonyms occur in many specialized search tasks, such as when searching medical documents, legal documents, and software engineering artifacts. We replicate prior w…
Expert-sourcing Domain-specific Knowledge: The Case of Synonym Validation
Michael Unterkalmsteiner, Andrew Yates
One prerequisite for supervised machine learning is high quality labelled data. Acquiring such data is, particularly if expert knowledge is required, costly or even impossible if t…
Zero-shot Query Contextualization for Conversational Search
Antonios Minas Krasakis, Andrew Yates, Evangelos Kanoulas
Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in con…
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Thong Nguyen, Andrew Yates, Ayah Zirikly +2
Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deploy…
Language Models As or For Knowledge Bases
Simon Razniewski, Andrew Yates, Nora Kassner +1
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we…
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a…
Personalized Entity Search by Sparse and Scrutable User Profiles
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories a…
How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset
Iain Mackie, Jeffery Dalton, Andrew Yates
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics b…
Simplified Data Wrangling with ir_datasets
Sean MacAvaney, Andrew Yates, Sergey Feldman +3
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, the…
Pretrained Transformers for Text Ranking: BERT and Beyond
Jimmy Lin, Rodrigo Nogueira, Andrew Yates
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, ins…
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Zhi Zheng, Kai Hui, Ben He +3
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant info…
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models…
STANCY: Stance Classification Based on Consistency Cues
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates +1
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classific…
Listening between the Lines: Learning Personal Attributes from Conversations
Anna Tigunova, Andrew Yates, Paramita Mirza +1
Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition o…
Investigating Retrieval Method Selection with Axiomatic Features
Siddhant Arora, Andrew Yates
We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combin…
Using Multi-Sense Vector Embeddings for Reverse Dictionaries
Michael A. Hedderich, Andrew Yates, Dietrich Klakow +1
Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings…
CEDR: Contextualized Embeddings for Document Ranking
Sean MacAvaney, Andrew Yates, Arman Cohan +1
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to thes…
Overcoming low-utility facets for complex answer retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Many questions cannot be answered simply; their answers must include numerous nuanced details and additional context. Complex Answer Retrieval (CAR) is the retrieval of answers to…
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Canjia Li, Yingfei Sun, Ben He +5
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new…
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates +1
Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these appro…
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Arman Cohan, Bart Desmet, Andrew Yates +3
Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for l…
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Sean MacAvaney, Bart Desmet, Arman Cohan +4
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the tempo…
Characterizing Question Facets for Complex Answer Retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR ba…
Depression and Self-Harm Risk Assessment in Online Forums
Andrew Yates, Arman Cohan, Nazli Goharian
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter an…