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 …

Abstract Information Extraction (IE) techniques are essential to gleaning valuable
information about entities and their relationships from unstructured text and creating a …

Identifying all names that refer to a particular set of named entities is a challenging task, as
quite often we need to consider many features that include a lot of variation like …

Search is one of the most quickly growing applications in the mobile market. As people rely
more on portable devices for performing search, it becomes increasingly important to …

Named entity extraction (NEE) and disambiguation (NED) are two areas of research that are
well covered in literature. Typical fields addressing these topics are information retrieval …

While building and using a fully semantic understanding of Web contents is a distant goal,
named entities (NEs) provide a small, tractable set of elements carrying a well-defined …

Most previous research treats named entity extraction and classification as an end-to-end
task. We argue that the two sub-tasks should be addressed separately. Entity extraction lies …

Disambiguating named entities in natural language texts maps ambiguous names to
canonical entities registered in a knowledge base such as DBpedia, Freebase, or YAGO …

Extracting named entities in text and linking extracted names to a given knowledge base are
fundamental tasks in applications for text understanding. Existing systems typically run a …

Named entity discovery (NED) is an important information retrieval problem that can be
decomposed into two sub-problems. The first sub-problem, named entity recognition (NER) …