Improving Early-Stage Retrieval with Entity Linking

Conceitos essenciais

Expanding the corpus with linked entity names can boost the performance of both sparse and dense retrievers in the early stage of cascaded ranking architectures.

Resumo

The content discusses the use of entity linking to improve the performance of information retrieval systems, particularly in the early stage of cascaded ranking pipelines.
Key highlights:

Traditional sparse retrievers like BM25 suffer from vocabulary mismatch issues, while dense retrievers offer improved performance but require more computational resources and training data.
The author proposes leveraging entity linking to expand the corpus (queries and passages) with relevant entity names, in an attempt to reduce semantic gaps and improve recall in the early retrieval stage.
Experiments are conducted on the MS MARCO passage dataset using BM25 as the sparse retriever and the STAR-ADORE pipeline as the dense retriever.
The entity-expanded runs, using both explicit and hashed entity names, are shown to retrieve complementary results compared to the non-expanded runs.
Run combination methods like classifier selection and Reciprocal Rank Fusion are used to maximize the benefits of entity linking for sparse retrieval.
While entity linking improves the recall of BM25, no significant change is observed in the overall performance of the dense retriever.

Estatísticas

The MS MARCO passage dataset is used for the experiments.
The original, MonoT5, and DuoT5 relevance judgments are utilized.

Citações

"Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval performance is restricted by semantic discrepancies and vocabulary gaps."
"Transformer-based dense retrievers introduce significant improvements in information retrieval tasks by exploiting low-dimensional contextualized representations of the corpus. While dense retrievers are known for their relative effectiveness, they suffer from lower efficiency and lack of generalization issues, when compared to sparse retrievers."

Principais Insights Extraídos De

Information Retrieval with Entity Linking

by Dahlia Sheha... às arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08678.pdf

Perguntas Mais Profundas

How can the proposed entity linking approach be extended to other datasets and domains beyond MS MARCO?

The proposed entity linking approach can be extended to other datasets and domains beyond MS MARCO by following a systematic process. Here are some steps to consider:

Data Preparation: Ensure that the new dataset is pre-processed and formatted in a way that is compatible with the entity linking system used in the study. This may involve tokenization, normalization, and cleaning of the text data.

Entity Recognition and Disambiguation: Apply the entity linking system to the new dataset to identify and disambiguate entity mentions. This step may require training the system on domain-specific data to improve accuracy.

Corpus Expansion: Expand both the queries and documents in the new dataset with linked entities in explicit and hashed formats, similar to the approach taken in the study. This step will enrich the dataset with additional semantic information.

Evaluation and Validation: Evaluate the performance of the entity linking approach on the new dataset using appropriate metrics and validation techniques. Compare the results with existing retrieval methods to assess the effectiveness of the approach.

Fine-tuning and Optimization: Fine-tune the entity linking system and the corpus expansion techniques based on the characteristics of the new dataset and domain. This may involve adjusting parameters, improving entity recognition models, or refining the hashing process.

Generalization and Adaptation: Ensure that the entity linking approach can generalize well to different datasets and domains by testing it on diverse datasets with varying characteristics. Adapt the approach as needed to address specific challenges or requirements of the new domain.

By following these steps and adapting the entity linking approach to the specific characteristics of the new dataset and domain, it can be successfully extended beyond MS MARCO to other contexts.

What are the potential limitations and drawbacks of using hashed entity names compared to explicit entity names for corpus expansion?

Using hashed entity names for corpus expansion has some potential limitations and drawbacks compared to using explicit entity names:

Loss of Interpretability: Hashed entity names do not provide any meaningful information about the entities they represent, making it challenging to interpret the expanded corpus.

Collision Risk: Hashing can lead to collisions where different entities are mapped to the same hash value, potentially causing confusion and inaccuracies in the expanded corpus.

Difficulty in Debugging: Debugging and error analysis become more challenging with hashed entity names, as it is harder to trace back to the original entities that were hashed.

Limited Flexibility: Hashed entity names do not allow for any flexibility in terms of adjusting or modifying the expanded corpus based on specific requirements or feedback.

Scalability Concerns: As the dataset grows, managing and updating hashed entity names can become complex and resource-intensive.

Impact on Retrieval Performance: The use of hashed entity names may impact retrieval performance, especially if the hashing process introduces noise or inaccuracies in the expanded corpus.

While hashed entity names offer benefits such as data privacy and reduced storage requirements, these limitations should be carefully considered when choosing between hashed and explicit entity names for corpus expansion.

How can the insights from this work be leveraged to develop hybrid retrieval models that seamlessly integrate sparse and dense retrievers for optimal performance?

The insights from this work can be leveraged to develop hybrid retrieval models that seamlessly integrate sparse and dense retrievers for optimal performance by following these steps:

Feature Fusion: Combine the strengths of sparse and dense retrievers by integrating features extracted from both models. This can include using sparse features for exact matching and dense features for semantic understanding.

Model Ensemble: Develop an ensemble model that leverages the outputs of both sparse and dense retrievers to make final ranking decisions. This can involve combining ranking scores or using a meta-classifier to merge results.

Adaptive Weighting: Dynamically adjust the weights assigned to sparse and dense retrieval components based on the characteristics of the query and document. This adaptive weighting can optimize performance for different types of information needs.

Feedback Mechanisms: Implement feedback mechanisms that allow the hybrid model to learn from user interactions and relevance feedback. This can help refine the retrieval process and improve performance over time.

Query Expansion: Use entity linking and other semantic enrichment techniques to expand queries and documents with additional context and information. This can enhance the understanding of the information need and improve retrieval accuracy.

Evaluation and Validation: Thoroughly evaluate the hybrid retrieval model using appropriate metrics and benchmarks to ensure that it outperforms individual sparse and dense retrievers. Fine-tune the model based on evaluation results to achieve optimal performance.

By incorporating these strategies and building upon the insights gained from the study, a hybrid retrieval model that seamlessly integrates sparse and dense retrievers can be developed to achieve optimal performance in information retrieval tasks.