Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …

An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …

With the increase of online customer opinions in specialised websites and social networks,
automatic systems to help organise and classify customer reviews by domain-specific aspect …

Word embeddings and pre-trained language models allow to build rich representations of
text and have enabled improvements across most NLP tasks. Unfortunately they are very …

NER model has achieved promising performance on standard NER benchmarks. However,
recent studies show that previous approaches may over-rely on entity mention information …

Extracting named entities text forms the basis for many crucial tasks such as information
retrieval and extraction, machine translation, opinion mining, sentiment analysis and …

Supervised named entity recognition (NER) in the biomedical domain depends on large sets
of annotated texts with the given named entities. The creation of such datasets can be time …

Zero-resource cross-lingual transfer approaches aim to apply supervised models from a
source language to unlabelled target languages. In this paper we perform an in-depth study …

Word embeddings, ie, low-dimensional vector representations such as GloVe and SGNS,
encode word" meaning" in the sense that distances between words' vectors correspond to …

The analysis of literary works has experienced a surge in computer-assisted processing. To
obtain insights into the community structures and social interactions portrayed in novels, the …