George Brokos


2018

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Deep Relevance Ranking Using Enhanced Document-Query Interactions
Ryan McDonald | George Brokos | Ion Androutsopoulos
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR’s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

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AUEB at BioASQ 6: Document and Snippet Retrieval
George Brokos | Polyvios Liosis | Ryan McDonald | Dimitris Pappas | Ion Androutsopoulos
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

We present AUEB’s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.