PACRR: A Position-Aware Neural IR Model for Relevance Matching
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.
Code (3)
Tasks
Ad-Hoc Information RetrievalInformation RetrievalPositionRetrievalSimilar Papers 제목 키워드 기반
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is also important. Intuitively, when extrac…
Ad-Hoc Information RetrievalRetrievalDeep Relevance Ranking Using Enhanced Document-Query Interactions
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…
Ad-Hoc Information RetrievalQuestion AnsweringDE-PACRR: Exploring Layers Inside the PACRR Model
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be …
Ad-Hoc Information RetrievalInformation RetrievalmodelRetrievalTITA: A Two-stage Interaction and Topic-Aware Text Matching Model
In this paper, we focus on the problem of keyword and document matching by considering different relevance levels. In our recommendation system, different people follow different hot keywords with interest. We need to at…
Text MatchingVocal Bursts Valence PredictionCompact Token Representations with Contextual Quantization for Efficient Document Re-ranking
Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted…
QuantizationRe-Ranking