Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases

Qi, Fanchao; Xie, Rong; Zang, Yuan; Liu, Zhiyuan; Sun, Maosong

doi:10.1007/s11704-020-0002-4

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…Sememe knowledge bases like HowNet (Dong and Dong, 2006) use a set of predefined sememes to annotate words, so that the meaning of a word can be precisely expressed by its sememes. With the help of such sememe knowledge bases, sememes have been successfully utilized in various NLP tasks (Qi et al, 2021a), including semantic composition (Qi et al, 2019), word sense disambiguation (Hou et al, 2020), reverse dictionary (Zhang et al, 2020a), backdoor learning (Qi et al, 2021b), etc.…”

Section: Incorporation Of Sememesmentioning

confidence: 99%

QuoteR: A Benchmark of Quote Recommendation for Writing

Qi¹,

Yang²,

Yi³

et al. 2022

Preprint

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It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes more efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets. To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese. Any part of it is larger than previous unpublished counterparts. We conduct an extensive evaluation of existing quote recommendation methods on QuoteR. Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR. All the code and data of this paper are available at https: //github.com/thunlp/QuoteR.

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Section: Incorporation Of Sememesmentioning

confidence: 99%

QuoteR: A Benchmark of Quote Recommendation for Writing

Qi¹,

Yang²,

Yi³

et al. 2022

Preprint

Self Cite

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“…Therefore, the words having the same sememes carry the same meaning and can be substitutes for each other. Following previous work , we use HowNet (Dong and Dong, 2006;Qi et al, 2019b) as the source of sememe annotations, which manually annotated sememes for more than 100, 000 English and Chinese words and has been applied to many NLP tasks (Qi et al, 2019a;Qin et al, 2020;Hou et al, 2020;Qi et al, 2020b). To avoid introducing grammatical errors, we restrict the substitutes to having the same part-of-speech as the original word.…”

Section: Candidate Substitute Generationmentioning

confidence: 99%

Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution

Qi¹,

Yao²,

Xu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released at https: //github.com/thunlp/BkdAtk-LWS.

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“…Second, the nature that limited sememes can represent meanings of unlimited words endows HowNet with the ability to handle low-data regimes, e.g., sememes can improve the embeddings of rare words (Niu et al, 2017). Because of these advantages, HowNet has been successfully utilized in various NLP tasks (Qi et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

Qi¹,

Lv²,

Liu³

et al. 2022

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In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks. However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. To address this issue, the task of sememe prediction for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically predicting sememes for a BabelNet synset, the words in many languages in the synset would obtain sememe annotations simultaneously. However, previous SPBS methods have not taken full advantage of the abundant information in BabelNet. In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS. We design a multimodal information fusion model to encode and combine this information for sememe prediction. Experimental results show the substantial outperformance of our model over previous methods (about 10 MAP and F1 scores). All the code and data of this paper can be obtained at https: //github.com/thunlp/MSGI.

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Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases

Cited by 13 publications

References 31 publications

QuoteR: A Benchmark of Quote Recommendation for Writing

QuoteR: A Benchmark of Quote Recommendation for Writing

Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

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