Results 1 to 10 of about 1,553 (186)
Sememe Heredity of Action Semantics: Evidence From the Priming Effect and Prospective Memory [PDF]
Frontiers in Psychology, 2020 The sememe heredity of action semantics may be affected by the related association of a verb or noun in an action phrase and the related association between one action phrase and another.
Zhanyu Yu, Yue Ma, Lijuan Wang
doaj +4 more sources
Enhanced Word-Unit Broad Learning System With Sememes [PDF]
IEEE Access, 2023 High accuracy in text classification can be achieved by simultaneously learning multiple sources of information, such as sequence and word. In this study, we propose a novel learning framework for text classification, called Word-unit Broad Learning ...
Yuchao Jiang +3 more
doaj +3 more sources
DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information [PDF]
Applied Sciences, 2020 Word embedding is an important reference for natural language processing tasks, which can generate distribution presentations of words based on many text data.
Shengwen Li +5 more
doaj +2 more sources
Sememe-Based Lexical Knowledge Representation Learning [PDF]
, 2023 AbstractLinguistic and commonsense knowledge bases describe knowledge in formal and structural languages. Such knowledge can be easily leveraged in modern natural language processing systems. In this chapter, we introduce one typical kind of linguistic knowledge (sememe knowledge) and a sememe knowledge base named HowNet.
Yujia Qin +3 more
openalex +2 more sources
Cross-lingual Lexical Sememe Prediction [PDF]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018 Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases.
Fanchao Qi +5 more
openalex +2 more sources
Modeling Semantic Compositionality with Sememe Knowledge [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models.
Fanchao Qi +6 more
+6 more sources
Improved Word Representation Learning with Sememes [PDF]
Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers), 2017 Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases.
Yilin Niu +3 more
openalex +2 more sources
TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction.
Comput Intell Neurosci, 2022 When TextRank algorithm based on graph model constructs graph associative edges, the co‐occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with ...
Zhou N, Shi W, Liang R, Zhong N.
europepmc +2 more sources
Incorporating Sememes into Chinese Definition Modeling [PDF]
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019 Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition.
Liner Yang +5 more
openalex +4 more sources
Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model [PDF]
Applied Sciences, 2020 Sememe is the smallest semantic unit for describing real-world concepts, which improves the interpretability and performance of Natural Language Processing (NLP).
Xiaojun Kang +6 more
doaj +2 more sources