Chinese Wordnet

Contextualized embeddings have proven to be powerful tools in various NLP tasks. However, their interpretability and how they encode lexical semantics remain challenging issues. In this paper, we tackle this problem by using definition modeling, a technique that aims to generate human-readable definitions for words, as a means to evaluate and understand high-dimensional semantic vectors. We introduce the Vec2Gloss model, which generates glosses from the contextualized embeddings of target words. The systematic gloss patterns provided by Chinese Wordnet enable us to examine the mechanism behind the model’s gloss generation. To delve deeper into this mechanism, we devise two dependency indices to measure the semantic and contextual dependencies of the generated glosses. These indices allow us to analyze the generated texts at both the gloss and token levels. Our results demonstrate that the proposed Vec2Gloss model enhances our understanding of lexical semantics in contextualized embeddings.

Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fast-changing language environment. Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphology-guided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN). This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model.

The present study describes recent developments of Chinese Wordnet, which has been reformatted using the lemon model and published as part of the Linguistic Linked Open Data Cloud. While lemon suffices for modeling most of the structures in Chinese Wordnet at the lexical level, the model does not allow for finergrained distinction of a word sense, or meaning facets, a linguistic feature also attended to in Chinese Wordnet. As for the representation of synsets, we use the WordNet RDF ontology for integration’s sake. Also, we use another ontology proposed by the Global WordNet Association to show how Chinese Wordnet as Linked Data can be integrated into the Global WordNet Grid.

Semantic relations of different types have played an important role in wordnet, and have been widely recognized in various fields. In recent years, with the growing interests of constructing semantic network in support of intelligent systems, automatic semantic relation discovery has become an urgent task. This paper aims to extract semantic relations relying on the in situ morpho-semantic structure in Chinese which can dispense of an outside source such as corpus or web data. Manual evaluation of thousands of word pairs shows that most relations can be successful predicted. We believe that it can serve as a valuable starting point in complementing with other approaches, which will hold promise for the robust lexical relations acquisition.

The present study proposes an innovative way of expanding the lexical repository of Chinese Wordnet (CWN). Fine-grained as the senses and sense facets of its entries are, the current status of CWN fails to include such high-frequency words as àiqíng (‘love’) and such high-familiarity words as guānxīn (‘to care’) due to the lac of linguistic manpower. In view of this limited inclusion of words on CWN, we propose to extend its lexical knowledge by constructing a wiki for CWN, or CWIKIN—a collaborative platform on which registered users can contribute to CWN by adding new entries, editing existing ones and rating one another’s contribution to ensure the quality of collective intelligence. What distinguishes CWIKIN from a typical wiki is that it presents synonymous sets of Chinese words that are currently only implicitly represented on CWN, and helps users in adding those words as well as various lexical semantic relations by suggesting potential equivalents along with their parts-of-speech, definitions and example sentences bootstrapped from Princeton WordNet via Sinica BOW, a bilingual ontological wordnet. We believe that the proposed platform will facilitate the enrichment of Chinese Lexical Resources in the context of Web collective intelligence and contribute to the advancement of Chinese Lexicography.

The representation of lexical semantic knowledge has been one of the most important research topics in the field of computational lexical semantics. Among relevant lexical resources, the design architecture of Princeton WordNet is the most popular one. In this paper, however, we argue that the current synset scheme requires more extensions when applied to the analysis of deeper sense structure in Chinese Wordnet. Issues involved include the underlying structure of sense, meaning facet and their relations. Based on a large amount of empirical analysis of sense data, this paper proposes a fine-grained framework in representing lexical semantic knowledge for Chinese Wordnet, which we believe will be an important consideration for the envisioned cross-lingual global wordnet grid construction. The systematic polysemy patterns found among meaning facets can also be used as a human gold standard of hand-annotated data for metonymy resolution task.

This presentation introduces a Python module (PyCWN) for accessing and processing Chinese lexical resources. In particular, our focus is put on the Chinese Wordnet (CWN) that has been developed and released by CWN group at Academia Sinica. PyCWN provides the access to Chinese Wordnet (sense and relation data) under the Python environment. The presenation further demonstrates how this module applies to a variety of lexical processing tasks as well as the potentials for multilingual lexical processing.

Lexical Markup Framework (LMF, ISO-24613) is the ISO standard which provides a common standardized framework for the construction of natural language processing lexicons. LMF facilitates data exchange among computational linguistic resources, and also promises a convenient uniformity for future application. This study describes the design and implementation of the WordNet-LMF used to represent lexical semantics in Chinese WordNet. The compiled CWN-LMF will be released to the community for linguistic researches.

Synset and semantic relation based lexical knowledge base such as wordnet, have been well-studied and constructed in English and other European languages (EuroWordnet). The Chinese wordnet (CWN) has been launched by Academia Sinica basing on the similar paradigm. The synset that each word sense locates in CWN are manually labeled, however, the lexical semantic relations among synsets are not fully constructed yet. In this present paper, we try to propose a lexical pattern-based algorithm which can automatically discover the semantic relations among verbs, especially the troponymy relation. There are many ways that the structure of a language can indicate the meaning of lexical items. For Chinese verbs, we identify two sets of lexical syntactic patterns denoting the concept of hypernymy-troponymy relation. We describe a method for discovering these syntactic patterns and automatically extracting the target verbs and their corresponding hypernyms. Our system achieves satisfactory results and we beleive it will shed light on the task of automatic acquisition of Chinese lexical semantic relations and ontology learning as well.

This paper reports our recent work to use visualization to present semantic relation for Chinese WordNet. We design a visualization interface, named CWN-Viz, based on "TouchGraph". There are three import design features of this visualization interface: First, visualization is driven by wordform, the most intuitive lexical search unit in Chinese. Second, the CWN-Viz allows visualization of bilingual semantic relations by incorporating Sinica BOW (Bilingual Ontological WordNet) information. Third, the semantic distance of each relation is calculated and used in both clustering and visualization. All rights are reserved.