# Automatic Construction of Sememe Knowledge Bases via Dictionaries

arXiv: https://arxiv.org/abs/2105.12585 · HTML: https://mlsanity.com/paper/2105.12585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 84 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://mlsanity.com/paper/1910.12196/) (2019)
- [Learning to Understand Phrases by Embedding the Dictionary](https://mlsanity.com/paper/1504.00548/) (2015)
- [OpenHowNet: An Open Sememe-based Lexical Knowledge Base](https://mlsanity.com/paper/1901.09957/) (2019)
- [Incorporating Glosses into Neural Word Sense Disambiguation](https://mlsanity.com/paper/1805.08028/) (2018)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [Learning to Compute Word Embeddings On the Fly](https://mlsanity.com/paper/1706.00286/) (2017)
- [Modeling Semantic Compositionality with Sememe Knowledge](https://mlsanity.com/paper/1907.04744/) (2019)
- [Using BERT for Word Sense Disambiguation](https://mlsanity.com/paper/1909.08358/) (2019)
- [Language Modeling with Sparse Product of Sememe Experts](https://mlsanity.com/paper/1810.12387/) (2018)
- [Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets](https://mlsanity.com/paper/1912.01795/) (2019)
- [Incorporating Chinese Characters of Words for Lexical Sememe Prediction](https://mlsanity.com/paper/1806.06349/) (2018)

## Built on by (most cited first, 1 of 1)

- [Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information](https://mlsanity.com/paper/2203.07426/) (2022)

## Abstract (arXiv)

A sememe is defined as the minimum semantic unit in linguistics. Sememe knowledge bases (SKBs), which comprise words annotated with sememes, enable sememes to be applied to natural language processing. So far a large body of research has showcased the unique advantages and effectiveness of SKBs in various tasks. However, most languages have no SKBs, and manual construction of SKBs is time-consuming and labor-intensive. To tackle this challenge, we propose a simple and fully automatic method of building an SKB via an existing dictionary. We use this method to build an English SKB and a French SKB, and conduct comprehensive evaluations from both intrinsic and extrinsic perspectives. Experimental results demonstrate that the automatically built English SKB is even superior to HowNet, the most widely used SKB that takes decades to build manually. And both the English and French SKBs can bring obvious performance enhancement in multiple downstream tasks. All the code and data of this paper (except the copyrighted dictionaries) can be obtained at https://github.com/thunlp/DictSKB.