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.

Most implemented papers

Text Matching as Image Recognition

NTMC-Community/MatchZoo 20 Feb 2016

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

Georgetown-IR-Lab/cedr 15 Apr 2019

We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).

A Self-Attentive model for Knowledge Tracing

shalini1194/SAKT 16 Jul 2019

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

khui/repacrr EMNLP 2017

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 30 Jun 2017

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 23 Nov 2017

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 26 Mar 2019

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 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

mikvrax/TrecingLab 28 Apr 2017

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 30 May 2017

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.