Ad-Hoc Information Retrieval
26 papers with code • 1 benchmarks • 0 datasets
Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Benchmarks
These leaderboards are used to track progress in Ad-Hoc Information Retrieval
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Most implemented papers
Text Matching as Image Recognition
An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
A Self-Attentive model for Knowledge Tracing
shalini1194/SAKT
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Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities.
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.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
khui/repacrr
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Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.
A Deep Relevance Matching Model for Ad-hoc Retrieval
sebastian-hofstaetter/neural-ranking-drmm
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Specifically, our model employs a joint deep architecture at the query term level for relevance matching.
Simple Applications of BERT for Ad Hoc Document Retrieval
castorini/birch
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Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.
Document Ranking with a Pretrained Sequence-to-Sequence Model
castorini/pygaggle
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Findings of the Association for Computational Linguistics 2020
We investigate this observation further by varying target words to probe the model's use of latent knowledge.
Neural Ranking Models with Weak Supervision
Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
geek-ai/irgan
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This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
