A Hypothesis of Neural Network about Chinese Language Understanding

2009 ◽  
Author(s):  
Qing-jiang Wang ◽  
Lin Zhang
Keyword(s):  
Neural Network ◽  
Chinese Language ◽  
Language Understanding

scholarly journals Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1230
Author(s):  
Anda Stoica ◽  
Tibor Kadar ◽  
Camelia Lemnaru ◽  
Rodica Potolea ◽  
Mihaela Dînşoreanu
Keyword(s):  
Neural Network ◽  
Error Analysis ◽  
Natural Language ◽  
Network Model ◽  
Network Architecture ◽  
Natural Language Understanding ◽  
Wide Spread ◽  
Neural Network Architecture ◽  
Language Understanding ◽  
Slot Filling

As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, integration of other less-known languages such as Romanian is still missing. This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. We propose a customized capsule neural network architecture that performs intent detection and slot filling in a joint manner and we evaluate how well it handles utterances containing various levels of complexity. The capsule network model shows a significant improvement in intent detection when compared to models built using the well-known Rasa NLU tool. Through error analysis, we observe clear error patterns that occur systematically. Variability in language when expressing one intent proves to be the biggest challenge encountered by the model.

A WORD-BASED CHINESE LANGUAGE UNDERSTANDING SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 25-35
Author(s):  
TIAN-SHUN YAO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Computer Programs ◽  
World Knowledge ◽  
Knowledge Source ◽  
Language Understanding ◽  
Language Analysis ◽  
The World

With the word-based theory of natural language processing, a word-based Chinese language understanding system has been developed. In the light of psychological language analysis and the features of the Chinese language, this theory of natural language processing is presented with the description of the computer programs based on it. The heart of the system is to define a Total Information Dictionary and the World Knowledge Source used in the system. The purpose of this research is to develop a system which can understand not only Chinese sentences but also the whole text.

scholarly journals Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiujin Yu ◽  
Shengfu Liu ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Recognition ◽  
High Efficiency ◽  
Chinese Language ◽  
Strong Support ◽  
Superior Performance ◽  
Feature Analysis ◽  
Data Mining Techniques ◽  
Self Organizing

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

scholarly journals A General Planning-Based Framework for Goal-Driven Conversation Assistant

2019 ◽  
Vol 33 ◽  
pp. 9857-9858
Author(s):  
Zhuoxuan Jiang ◽  
Jie Ma ◽  
Jingyi Lu ◽  
Guangyuan Yu ◽  
Yipeng Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
General Framework ◽  
Planning Method ◽  
Conversational Agents ◽  
World Model ◽  
Language Understanding ◽  
Dialogue Management ◽  
Language Generation ◽  
Planning Methods

We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains.

scholarly journals Using Deep Time Delay Neural Network for Slot Filling in Spoken Language Understanding

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 993
Author(s):  
Zhen Zhang ◽  
Hao Huang ◽  
Kai Wang
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Target Word ◽  
State Of The Art ◽  
Contextual Information ◽  
Spoken Language ◽  
Continuous Speech Recognition ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Slot Filling

Modeling the context of a target word is of fundamental importance in predicting the semantic label for slot filling task in Spoken Language Understanding (SLU). Although Recurrent Neural Network (RNN) has shown to successfully achieve the state-of-the-art results for SLU, and Bidirectional RNN is capable of obtaining further improvement by modeling information not only from the past, but also from the future, they only consider limited contextual information of the target word. In order to make the network deeper and hence obtain longer contextual information, we propose to use a multi-layer Time Delay Neural Network (TDNN), which is prevalent in current large vocabulary continuous speech recognition tasks. In particular, we use a TDNN with symmetric time delay offset. To make the stacked TDNN easily trained, residual structures and skip concatenation are adopted. In addition, we further improve the model by introducing ResTDNN-BiLSTM, which combines the advantages of both the residual TDNN and BiLSTM. Experiments on slot filling tasks on the Air Travel Information System (ATIS) and Snips benchmark datasets show the proposed SC-TDNN-C achieves state-of-the-art results without any additional knowledge and data resources. Finally, we review and compare slot filling results by using a variety of existing models and methods.

A NEW NEURAL NETWORK ARCHITECTURE WITH ASSOCIATIVE MEMORY, PRUNING AND ORDER-SENSITIVE LEARNING

1999 ◽  
Vol 09 (04) ◽  
pp. 351-370 ◽  
Author(s):  
M. SREENIVASA RAO ◽  
ARUN K. PUJARI
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Network Architecture ◽  
Hopfield Network ◽  
New Paradigm ◽  
Stable States ◽  
Neural Network Architecture ◽  
Language Understanding ◽  
Structure Database ◽  
Learning Technique

A new paradigm of neural network architecture is proposed that works as associative memory along with capabilities of pruning and order-sensitive learning. The network has a composite structure wherein each node of the network is a Hopfield network by itself. The Hopfield network employs an order-sensitive learning technique and converges to user-specified stable states without having any spurious states. This is based on geometrical structure of the network and of the energy function. The network is so designed that it allows pruning in binary order as it progressively carries out associative memory retrieval. The capacity of the network is 2n, where n is the number of basic nodes in the network. The capabilities of the network are demonstrated by experimenting on three different application areas, namely a Library Database, a Protein Structure Database and Natural Language Understanding.

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