Over the last couple of decades, community question‐answering sites (CQAs) have been a
topic of much academic interest. Scholars have often leveraged traditional machine learning …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Recently, the character-word lattice structure has been proved to be effective for Chinese
named entity recognition (NER) by incorporating the word information. However, since the …

In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …

Recently, many works have tried to augment the performance of Chinese named entity
recognition (NER) using word lexicons. As a representative, Lattice-LSTM (Zhang and Yang …

Recently, word enhancement has become very popular for Chinese Named Entity
Recognition (NER), reducing segmentation errors and increasing the semantic and …

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that
sequentially track character and word information have achieved great success. However …

Character-level Chinese named entity recognition (NER) that applies long short-term
memory (LSTM) to incorporate lexicons has achieved great success. However, this method …

Smart speakers such as Alexa and Google Home introduced Artificial Intelligence (AI) in
millions soon billions of households, making AI an everyday experience. We can now look …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …