It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic
class described by given seed entities. Various Natural Language Processing (NLP) and …

Low-resource Complex Named Entity Recognition aims to detect entities with the form of any
linguistic constituent under scenarios with limited manually annotated data. Existing studies …

Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target
semantic class described by seed entities. Most existing ESE methods are retrieval-based …

We address some of the limitations of coverage-based search result diversification models,
which often consist of separate components and rely on external systems for query aspects …

We propose AutoName, an unsupervised framework that extracts a name for a set of query
entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to …

Understanding the significance of entities in the context of a particular search topic
necessitates a special focus on linguistic, cultural, and national aspects. This paper presents …

Recent advances in text representation have shown that training on large amounts of text is
crucial for natural language understanding. However, models trained without predefined …

Input representation has a major impact on the effectiveness of Information Retrieval (IR)
systems. Further, developing a context-aware input representation for IR systems is crucial …