Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality
tabular data for model training remains a significant obstacle. Numerous works have …

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve
Natural Language (NL)-related tasks. However, there is still much to learn about how well …

Abstract Language models pretrained on large collections of tabular data have
demonstrated their effectiveness in several downstream tasks. However, many of these …

Tabular data is a way to structure, organize, and present information conveniently and
effectively. Real-world tables present data in two dimensions by arranging cells in matrices …

Named geographic entities (geo-entities for short) are the building blocks of many
geographic datasets. Characterizing geo-entities is integral to various application domains …

Table search aims to answer a query with a ranked list of tables. Unfortunately, current test
corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are …

Query and point of interest (POI) matching is a core task in location-based services~(LBS),
eg, navigation maps. It connects users' intent with real-world geographic information. Lately …

Tabular data pervades the landscape of the World Wide Web, playing a foundational role in
the digital architecture that underpins online information. Given the recent influence of large …

Considering the multimodal signals of search items is beneficial for retrieval effectiveness.
Especially in web table retrieval (WTR) experiments, accounting for multimodal properties of …

Entity matching (EM) identifies data records that refer to the same real-world entity. Despite
the effort in the past years to improve the performance in EM, the existing methods still …