2018/09/11 by Shu Liu, Liu, Shu, Jingjing Xu +5 · 5 citations
Computer Science · #Artificial intelligence #Artificial neural network #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Image (mathematics) #Linguistics #Matching (statistics) #Natural Language Processing Techniques #Natural language processing #Rationality #Semantic similarity #Sentence #Similarity (geometry) #Software Engineering Research #Topic Modeling #Word (group theory) #cs.CL
paper · pdf · doi:10.48550/arxiv.1809.03999
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/09/11 · openalex publication_date 2018/09/11 · arxiv updated 2018/09/12 · openalex created_date 2018/09/27 · openalex updated_date 2026/08/04
Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot well identify whether a sentence is semantically rational. The methods based on the language model do not measure the sentence by rationality but by commonness. The methods based on the similarity with human written sentences will fail if human-written references are not available. In this paper, we propose a novel model called Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality evaluation by taking advantage of sememe knowledge base HowNet. The advantage is that our model can utilize a proper combination of sememes to represent the fine-grained semantic meanings of a word within the specific contexts. We use the fine-grained semantic representation to help the model learn the semantic dependency among words. To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset. Experimental results on this dataset show that the proposed model outperforms the competitive baselines with a 5.4% improvement in accuracy.