An Empirical Study of Learning to Rank for Entity Search

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Word-Entity Duet Representations for Document Ranking (2017, methodology): “s in neural ranking with word embeddings can be incorporated [9]; be−er knowledge graph embeddings can be used [13]; be−er entity search methods can be applied when extracting word to entity features [3]; the a−ention mechanism can be extended to document’s entity-based representations. More sophist”
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, background): “Freebase entities that are retrieved by the query [6] or frequently appear in top retrieved documents’ annotations [10] are usually related to the query’s information needs; better expansion terms can be found in these related entities’ descriptions [26].”