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CEDR: Contextualized Embeddings for Document Ranking
This work investigates how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking and proposes a joint approach that incorporates BERT's classification vector into existing neural models and shows that it outperforms state-of-the-art ad-Hoc ranking baselines. Expand
Depression and Self-Harm Risk Assessment in Online Forums
This work introduces a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users, and proposes methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrates that this approach outperforms strong previously proposed methods. Expand
PACRR: A Position-Aware Neural IR Model for Relevance Matching
This work proposes a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document and yields better results under multiple benchmarks. Expand
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
A neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources is presented, which derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Expand
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
This work highlights three potential shortcomings caused by not considering context information and proposes three neural ingredients to address them: a disambiguation component, cascade k-max pooling, and a shuffling combination layer that yields Co-PACER, a novel context-aware neural IR model. Expand
SMHD: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
This paper investigates the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtains high-quality labeled data without the need for manual labelling. Expand
ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites
We automatically extract adverse drug reactions (ADRs) from consumer reviews provided on various drug social media sites to identify adverse reactions not reported by the United States Food and DrugExpand
PARADE: Passage Representation Aggregation for Document Reranking
We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a documentExpand
(Not Too) Personalized Learning to Rank for Contextual Suggestion
This work emphasizes how to merge and re-rank contextual suggestions from the open Web based on a user's personal interests by identifying context-independent queries, combining them with location information, and issuing the combined queries to multiple Web search engines. Expand
Pretrained Transformers for Text Ranking: BERT and Beyond
This tutorial provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example, and covers a wide range of techniques. Expand