Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

Named entity recognition (NER) task aims at identifying entities from a piece of text that
belong to predefined semantic types such as person, location, organization, etc. The state-of …

Many deep learning architectures have been proposed to model the compositionality in text
sequences, requiring a substantial number of parameters and expensive computations …

We describe a simple neural language model that relies only on character-level inputs.
Predictions are still made at the word-level. Our model employs a convolutional neural …

In this paper, we study a novel approach for named entity recognition (NER) and mention
detection in natural language processing. Instead of treating NER as a sequence labelling …

This lucid, accessible introduction to supervised machine learning presents core concepts in
a focused and logical way that is easy for beginners to follow. The author assumes basic …

In this paper, we present a new data set, named FreebaseQA, for open-domain factoid
question answering (QA) tasks over structured knowledge bases, like Freebase. The data …

Federated Learning (FL) is a technique to train models on distributed edge devices with
local data samples. Differential Privacy (DP) can be applied with FL to provide a formal …

In this paper, we propose a novel neural network structure, namely\emph {feedforward
sequential memory networks (FSMN)}, to model long-term dependency in time series …