Conference
Australasian Document Computing Symposium
About: Australasian Document Computing Symposium is an academic conference. The conference publishes majorly in the area(s): Relevance (information retrieval) & Ranking (information retrieval). Over the lifetime, 292 publications have been published by the conference receiving 2696 citations.
Topics: Relevance (information retrieval), Ranking (information retrieval), Computer science, Web search query, Query expansion
Papers published on a yearly basis
Papers
26 Nov 2014
TL;DR: This investigation finds that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective and that it remains unclear which function is best over-all.
Abstract: Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.
214 citations
1 Dec 1993
205 citations
Proceedings Article•
24 Nov 2015
178 citations
8 Dec 2015
TL;DR: This paper presents a benchmark dataset, CQADupStack, for use in community question-answering (cQA) research, which contains threads from twelve StackExchange subforums, annotated with duplicate question information.
Abstract: This paper presents a benchmark dataset, CQADupStack, for use in community question-answering (cQA) research. It contains threads from twelve StackExchange subforums, annotated with duplicate question information. We provide pre-defined training and test splits, both for retrieval and classification experiments, to ensure maximum comparability between different studies using the set. Furthermore, it comes with a script to manipulate the data in various ways. We give an analysis of the data in the set, and report benchmark results on a duplicate question retrieval task using well established retrieval models.
114 citations
Proceedings Article•
7 Dec 2009TL;DR: The authors' PMI score, computed using word-pair co-occurrence statistics from external data sources, has relatively good agreement with human scoring and it is shown that the ability to identify less useful topics can improve the results of a topic-based document similarity metric.
Abstract: Topic models can learn topics that are highly interpretable, semantically-coherent and can be used similarly to subject headings. But sometimes learned topics are lists of words that do not convey much useful information. We propose models that score the usefulness of topics, including a model that computes a score based on pointwise mutual information (PMI) of pairs of words in a topic. Our PMI score, computed using word-pair co-occurrence statistics from external data sources, has relatively good agreement with human scoring. We also show that the ability to identify less useful topics can improve the results of a topic-based document similarity metric.
108 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 5 |
| 2019 | 5 |
| 2018 | 13 |
| 2017 | 12 |
| 2016 | 15 |
| 2015 | 21 |