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Towards Building a Multilingual Sememe Knowledge Base Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets Fanchao Qi1∗, Liang Chang2∗y, Maosong Sun13z, Sicong Ouyang2y, Zhiyuan Liu1 1Department of Computer Science and Technology, Tsinghua University Institute for Artificial Intelligence, Tsinghua University Beijing National Research Center for Information Science and Technology 2Beijing University of Posts and Telecommunications 3Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University [email protected], [email protected] fsms, [email protected], [email protected] Abstract word husband A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain sense "married man" "carefully use" words annotated with sememes, have been successfully ap- plied to many NLP tasks. However, existing sememe KBs are built on only a few languages, which hinders their widespread human economize utilization. To address the issue, we propose to build a uni- sememe fied sememe KB for multiple languages based on BabelNet, a family male spouse multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. It man- ually annotates sememes for over 15 thousand synsets (the entries of BabelNet). Then, we present a novel task of auto- Figure 1: Sememe annotation of the word “husband” in matic sememe prediction for synsets, aiming to expand the HowNet. seed dataset into a usable KB. We also propose two simple and effective models, which exploit different information of synsets. Finally, we conduct quantitative and qualitative anal- sememes to annotate senses of over 100 thousand Chinese yses to explore important factors and difficulties in the task. and English words. Figure 1 illustrates an example of how All the source code and data of this work can be obtained on words are annotated with sememes in HowNet. https://github.com/thunlp/BabelNet-Sememe-Prediction. Different from most linguistic KBs like WordNet (Miller 1995), which explain meanings of words by word-level re- Introduction lations, sememe KBs like HowNet provide intensional defi- nitions for words using infra-word sememes. Sememe KBs A word is the smallest element of human languages that can have two unique strengths. The first one is their sememe-to- stand by itself, but not the smallest indivisible semantic unit. word semantic compositionality, which endows them with In fact, the meaning of a word can be divided into smaller special suitability for integration into neural networks (Gu components. For example, one of the meanings of “man” et al. 2018; Qi et al. 2019a). The second one is their na- can be represented as the composition of the meanings ture that limited sememes can represent unlimited meanings, sememe of “human”, “male” and “adult”. In linguistics, a which makes sememes very useful in low data regimes, e.g., (Bloomfield 1926) is defined as the minimum semantic unit improving embeddings of low-frequency words (Sun and arXiv:1912.01795v1 [cs.CL] 4 Dec 2019 of human languages. Some linguists believe that meanings Chen 2016; Niu et al. 2017). In fact, sememe KBs have been of all the words in any language can be decomposed of a proven beneficial to various NLP tasks such as word sense limited set of predefined sememes, which is related to the disambiguation (Duan, Zhao, and Xu 2007) and sentiment idea of universal semantic primitives (Wierzbicka 1996). analysis (Fu et al. 2013). Sememes are implicit in words. To utilize them in practi- Most languages have no sememe KBs, which prevents cal applications, people manually annotate words with pre- NLP applications of these languages benefiting from se- defined sememes to construct sememe knowledge bases meme knowledge. However, building a sememe KB for a (KBs). HowNet (Dong and Dong 2003) is the most fa- new language from scratch is time-consuming and labor- mous one, which uses about 2; 000 language-independent intensive — the construction of HowNet takes several lin- ∗Indicates equal contribution guistic experts more than two decades. To tackle this chal- yWork done during internship at Tsinghua University lenge, Qi et al. (2018) present the task of cross-lingual lexi- zCorresponding author cal sememe prediction (CLSP), aiming to facilitate the con- Copyright c 2020, Association for the Advancement of Artificial struction of a new language’s sememe KB by predicting se- Intelligence (www.aaai.org). All rights reserved. memes for words in that language. However, CLSP can pre- proposing two simple and effective models and conducting bn:00045106n human detailed analyses of factors in SPBS. en: husband, hubby annotate family zh: 丈夫, ⽼公, 先⽣, 夫 Dataset and Task Formalization fr: mari, époux, marié male BabelSememe Dataset de: Ehemann, Gemahl, Gatte …… spouse We build the BabelSememe dataset, which is expected to be the seed of a multilingual sememe KB and expanded A BabelNet synset sememes steadily by automatic sememe prediction together with ex- amination of humans. Sememe annotation in HowNet em- bodies hierarchical structures of sememes, as shown in Fig- Figure 2: Annotating sememes for the BabelNet synset ure 1. Nevertheless, considering the structures of sememes whose ID is bn:00045106n. The synset comprises words are seldom used in existing applications (Niu et al. 2017; in different languages (multilingual synonyms) having the Qi et al. 2019a) and it is very hard for ordinary people to same meaning “the man a woman is married to”, and they make structured sememe annotation, we ignore them in Ba- share the four sememes on the right part. belSememe. Thus, each BabelNet synset in BabelSememe is annotated with a set of sememes (as shown in Figure 2). In the following part, we elaborate how we build the dataset dict sememes for only one language at a time, which means and provide its statistics. repetitive efforts, including manual correctness checking conducted by native speakers, are required when construct- ing sememe KBs for multiple languages. Selecting Target Synsets We first select 20 thousand To solve this problem, we propose to build a unified se- synsets as target synsets. Each of them includes English and 1 meme KB for multiple languages, namely a multilingual Chinese synonyms annotated with sememes in HowNet, so sememe KB, based on BabelNet (Navigli and Ponzetto that we can generate candidate sememes for them. 2012a), which is a more economical and efficient way to transfer sememe knowledge to other languages. BabelNet is Generating Candidate Sememes We generate candidate a multilingual encyclopedic dictionary and comprises over sememes for each target synset using the sememes annotated 15 million entries called BabelNet synsets. Each BabelNet to its synonyms. Some sememes of the synonyms in a target synset contains words in multiple languages with the same synset should be annotated to the target synset. For exam- meaning (multilingual synonyms), and they should have the ple, the word “husband” is annotated with five sememes in same sememe annotation. Therefore, building a multilingual HowNet (Figure 1) in total, and the four sememes annotated sememe KB by annotating sememes for BabelNet synsets to the sense “married man” should be annotated to the Ba- can actually provide sememe annotation for words in multi- belNet synset bn:00045106n (Figure 2). Thus, we group the ple languages simultaneously (Figure 2 shows an example). sememes of all the synonyms in a target synset together to To advance the creation of a multilingual sememe KB, we form the candidate sememe set of the target synset. build a seed dataset named BabelSememe, which contains about 15 thousand BabelNet synsets manually annotated Annotating Appropriate Sememes We ask more than with sememes. Also, we present a novel task of automatic 100 annotation participants to select appropriate sememes sememe prediction for BabelNet synsets (SPBS), aiming to from corresponding candidate sememe set for each target gradually expand the seed dataset into a usable multilingual synset. All the participants have a good grasp of both Chi- sememe KB. In addition, we exploratively put forward two nese and English. We show them Chinese and English syn- simple and effective models for SPBS, which utilize differ- onyms as well as definitions of each synset, making sure ent information incorporated in BabelNet synsets. The first its meaning is fully understood. When annotation is fin- model exploits semantic information and recommends sim- ished, each target synset has been annotated by at least three ilar sememes to semantically close BabelNet synsets, while participants. We remove the synsets whose inter-annotator the second uses relational information and directly predicts agreement (IAA) is poor, where we use Krippendorff’s alpha relations between sememes and BabelNet synsets. In ex- coefficient (Krippendorff 2013) to measure IAA. Finally, periments, we evaluate sememe prediction performance of 15; 756 BabelNet synsets are retained, which are annotated the two models on BabelSememe, finding they achieve sat- with 43; 154 sememes selected from 104; 938 candidate se- isfying results. Moreover, the ensemble of the two models memes, and their average Krippendorff’s alpha coefficient is yield obvious performance enhancement. Finally, we con- 0.702. duct detailed quantitative and qualitative analyses to explore the factors influencing sememe prediction results, aiming to Dataset Statistics Detailed statistics of BabelNet synsets point out the characteristics and difficulties
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