Topic memory networks for short text classification
Many classification models work poorly on short texts due to data sparsity. To address this
issue, we propose topic memory networks for short text classification with a novel topic …
issue, we propose topic memory networks for short text classification with a novel topic …
From statistical methods to deep learning, automatic keyphrase prediction: A survey
Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a
given document. Recently, researchers have conducted in-depth studies on this task from …
given document. Recently, researchers have conducted in-depth studies on this task from …
Neural keyphrase generation via reinforcement learning with adaptive rewards
Generating keyphrases that summarize the main points of a document is a fundamental task
in natural language processing. Although existing generative models are capable of …
in natural language processing. Although existing generative models are capable of …
Summarizing medical conversations via identifying important utterances
Summarization is an important natural language processing (NLP) task in identifying key
information from text. For conversations, the summarization systems need to extract salient …
information from text. For conversations, the summarization systems need to extract salient …
Topic-aware neural keyphrase generation for social media language
A huge volume of user-generated content is daily produced on social media. To facilitate
automatic language understanding, we study keyphrase prediction, distilling salient …
automatic language understanding, we study keyphrase prediction, distilling salient …
Incorporating context-relevant concepts into convolutional neural networks for short text classification
Text classification is an important task in natural language processing. Previous text
classification models do not perform well on short texts due to the data sparsity problem. In …
classification models do not perform well on short texts due to the data sparsity problem. In …
Generative non-autoregressive unsupervised keyphrase extraction with neural topic modeling
X Zhu, Y Lou, J Zhao, W Gao, H Deng - Engineering Applications of …, 2023 - Elsevier
Unsupervised keyphrase extraction (UKE) aims to detect out a set of keyphrases of a
document without using any annotation signal for training the UKE model. Existing UKE …
document without using any annotation signal for training the UKE model. Existing UKE …
Keyword extraction: a modern perspective
T Nomoto - SN Computer Science, 2022 - Springer
The goal of keyword extraction is to extract from a text, words, or phrases indicative of what it
is talking about. In this work, we look at keyword extraction from a number of different …
is talking about. In this work, we look at keyword extraction from a number of different …
Emotion-infused deep neural network for emotionally resonant conversation
YC Chang, YC Hsing - Applied Soft Computing, 2021 - Elsevier
The widespread development of conversational agents (chatbots) has enabled us to
communicate and collaborate with different forms and functions of robots using natural …
communicate and collaborate with different forms and functions of robots using natural …
AMFF: A new attention-based multi-feature fusion method for intention recognition
C Liu, X Xu - Knowledge-based systems, 2021 - Elsevier
Intention recognition is based on a dialog between users to identify their real intentions,
which plays a key role in the question answering system. However, the content of a dialog is …
which plays a key role in the question answering system. However, the content of a dialog is …