Parallel Traversal of Large Ensembles of Decision Trees

Lettich, Francesco; Lucchese, Claudio; Nardini, Franco Maria; Orlando, Salvatore; Perego, Raffaele; Tonellotto, Nicola; Venturini, Rossano

doi:10.1109/tpds.2018.2860982

Cited by 18 publications

(14 citation statements)

References 22 publications

(30 reference statements)

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“…While the efficiency of learning to rank solutions for document re-ranking have been extensively studied [41,91,197], computational efficiency concerns have largely be ignored by prior work in neural ranking, prompting some to call for more attention to this matter [68,98]. That being said, some efforts do exist.…”

Section: Background and Preliminariesmentioning

confidence: 99%

Effective and practical neural ranking

MacAvaney

2021

SIGIR Forum

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Supervised machine learning methods that use neural networks ("deep learning") have yielded substantial improvements to a multitude of Natural Language Processing (NLP) tasks in the past decade. Improvements to Information Retrieval (IR) tasks, such as ad-hoc search, lagged behind those in similar NLP tasks, despite considerable community efforts. Although there are several contributing factors, I argue in this dissertation that early attempts were not more successful because they did not properly consider the unique characteristics of IR tasks when designing and training ranking models. I first demonstrate this by showing how large-scale datasets containing weak relevance labels can successfully replace training on in-domain collections. This technique improves the variety of queries encountered when training and helps mitigate concerns of over-fitting particular test collections. I then show that dataset statistics available in specific IR tasks can be easily incorporated into neural ranking models alongside the textual features, resulting in more effective ranking models. I also demonstrate that contextualized representations, particularly those from transformer-based language models, considerably improve neural ad-hoc ranking performance. I find that this approach is neither limited to the task of ad-hoc ranking (as demonstrated by ranking clinical reports) nor English content (as shown by training effective cross-lingual neural rankers). These efforts demonstrate that neural approaches can be effective for ranking tasks. However, I observe that these techniques are impractical due to their high query-time computational costs. To overcome this, I study approaches for offloading computational cost to index-time, substantially reducing query-time latency. These techniques make neural methods practical for ranking tasks. Finally, I take a deep dive into better understanding the linguistic biases of the methods I propose compared to contemporary and traditional approaches. The findings from this analysis highlight potential pitfalls of recent methods and provide a way to measure progress in this area going forward.

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Section: Background and Preliminariesmentioning

confidence: 99%

Effective and practical neural ranking

MacAvaney

2021

SIGIR Forum

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“…the same query, while others combine linearly this score with the overall document relevance score [3,7,25,50,52]. More recently, learning-to-rank algorithms are deployed in many information retrieval systems [8,18,46] to rank documents.…”

Section: Related Workmentioning

confidence: 99%

Weighting Passages Enhances Accuracy

Muntean

Nardini

Perego

et al. 2020

ACM Trans. Inf. Syst.

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We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.

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“…The main reason of this success is the ability of deep neural networks to understand complex language patterns and learn to extract features from text that allow them to match queries and documents by abstracting from their lexical representation. In the same time frame, feature-based LtR methods reached maturity, and research on this area focused primarily on specific aspects such as efficiency [10,39,45,46,74,80], diversification [70], permutation-invariant models [62,64]. An investigated topic in feature-based LtR was also how to reduce the performance gap between neural and ensemblebased models [66].…”

Section: Introductionmentioning

confidence: 99%