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Found 10 papers, 6 papers with code
MMEAD: MS MARCO Entity Annotations and Disambiguations
MMEAD, or MS MARCO Entity Annotations and Disambiguations, is a resource for entity links for the MS MARCO datasets.
Entity-aware Transformers for Entity Search
Pre-trained language models such as BERT have been a key ingredient to achieve state-of-the-art results on a variety of tasks in natural language processing and, more recently, also in information retrieval. Recent research even claims that BERT is able to capture factual knowledge about entity relations and properties, the information that is commonly obtained from knowledge graphs.
ORCAS-I: Queries Annotated with Intent using Weak Supervision
User intent classification is an important task in information retrieval.
Ranked #1 on
Intent Classification
on ORCAS-I
Conversational Entity Linking: Problem Definition and Datasets
Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting.
Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph
We review existing definitions of bias in the literature: people bias, algorithm bias, and a combination of the two, and further propose different strategies for tackling these biases for conversational search systems.
REL: An Entity Linker Standing on the Shoulders of Giants
Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits.
Ranked #3 on
Entity Linking
on Derczynski
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings.
Information search in a professional context - exploring a collection of professional search tasks
Search conducted in a work context is an everyday activity that has been around since long before the Web was invented, yet we still seem to understand little about its general characteristics.
Efficient Parallel Learning of Word2Vec
Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to produce state-of-art results for various Natural Language Processing tasks.
Runtime Optimizations for Prediction with Tree-Based Models
Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains.
