A two-domain coordinated sentence similarity scheme for question-answering robots regarding unpredictable outliers and non-orthogonal categories

2021 ◽  
Author(s):  
Boyang Li ◽  
Weisheng Xu ◽  
Zhiyu Xu ◽  
Jiaxin Li ◽  
Peng Peng
Keyword(s):  
Question Answering ◽  
Sentence Similarity

Sentence Similarity Algorithm Based on Fused Bi-Channel Dependency Matching Feature

2019 ◽  
Vol 34 (07) ◽  
pp. 2050019
Author(s):  
Xiaohan Guan ◽  
Jianhui Han ◽  
Zhi Liu ◽  
Mengmeng Zhang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Calculation Methods ◽  
Matching Problem ◽  
Position Information ◽  
The Past ◽  
Vector Interaction ◽  
Sentence Similarity ◽  
Similarity Algorithm

Many tasks of natural language processing such as information retrieval, intelligent question answering, and machine translation require the calculation of sentence similarity. The traditional calculation methods used in the past could not solve semantic understanding problems well. First, the model structure based on Siamese lack of interaction between sentences; second, it has matching problem which contains lacking position information and only using partial matching factor based on the matching model. In this paper, a combination of word and word’s dependence is proposed to calculate the sentence similarity. This combination can extract the word features and word’s dependency features. To extract more matching features, a bi-directional multi-interaction matching sequence model is proposed by using word2vec and dependency2vec. This model obtains matching features by convolving and pooling the word-granularity (word vector, dependency vector) interaction sequences in two directions. Next, the model aggregates the bi-direction matching features. The paper evaluates the model on two tasks: paraphrase identification and natural language inference. The experimental results show that the combination of word and word’s dependence can enhance the ability of extracting matching features between two sentences. The results also show that the model with dependency can achieve higher accuracy than these models without using dependency.

Sentence Similarity Metric and its Application in FAQ System

2013 ◽  
Vol 718-720 ◽  
pp. 2248-2251
Author(s):  
Pei Ying Zhang
Keyword(s):  
Semantic Similarity ◽  
Question Answering ◽  
Similarity Metric ◽  
Question Answering System ◽  
Sentence Similarity

FAQ system is a question answering system which finds the question sentence from question-answer collection and then returns its corresponding answer to user. The task of matching questions to corresponding question-answer pairs has become a major challenge in FAQ system. This paper proposes a method for sentence similarity metric between questions according to its semantic similarity as well as the length of question length. Experiments show that this method can improve the accuracy and intelligence of answering system, has some practical value.

scholarly journals Sentence Similarity Computation in Question Answering Robot

2019 ◽  
Vol 1237 ◽  
pp. 022093 ◽  
Author(s):  
Shijing Si ◽  
Weiguo Zheng ◽  
Liuyang Zhou ◽  
Mei Zhang
Keyword(s):  
Question Answering ◽  
Sentence Similarity ◽  
Similarity Computation

Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism

2021 ◽  
Vol 20 (1) ◽  
pp. 1-21
Author(s):  
Peiying Zhang ◽  
Xingzhe Huang ◽  
Maozhen Li ◽  
Yu Xue
Keyword(s):  
Neural Networks ◽  
Customer Service ◽  
Language Processing ◽  
Question Answering ◽  
Hybrid Algorithms ◽  
Attention Mechanism ◽  
Similarity Analysis ◽  
Model Based ◽  
Sentence Similarity ◽  
Nature Inspired Algorithms

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Sign in / Sign up

Export Citation Format

Share Document