This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …

Development sets are impractical to obtain for real low-resource languages, since using all
available data for training is often more effective. However, development sets are widely …

Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to
an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is …

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language
knowledge base (KB) for mentions extracted from source-language texts. The first step of (X) …

Inference in structured prediction involves finding the best output structure for an input,
subject to certain constraints. Many current approaches use sequential inference, which …

In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of
name transliteration in a many-to-one multilingual paradigm, transliterating from 590 …

Transliteration is an important task in natural language processing (NLP) which aims to
convert a name in the source language to the target language without changing its …

Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign
language text into an English knowledge base such as Wikipedia, has seen a lot of research …

To improve deep-learning performance in low-resource settings, many researchers have
redesigned model architectures or applied additional data (eg, external resources …

Content on the web is predominantly written in English, making it inaccessible to those who
only speak other languages. Knowledge graphs can store multilingual information, facilitate …