Sememes are defined as the minimum semantic units of human languages. People have manually annotated lexical sememes for words and form linguistic knowledge bases. However, manual construction is time-consuming and labor-intensive, with significant annotation inconsistency and noise. In this paper, we for the first time explore to automatically predict lexical sememes based on semantic meanings...

In this paper, we firstly describe a novel approach to calculate the Chinese sememe similarity based on the HowNet hierarchical sememe tree. When we calculate the sememe similarity, we not only take Semantic Distance, Node Depth and Semantic Coincidence Degree into consideration, but also propose two impact factors named Node Environment Dense (NED) and Node Layer Ratio (NLR) to optimize the ca...

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. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-d...

one may daresay that the foremost issue and problem in translation is to observe “translation equivalence principle” so that the translated text to be the equivalent and all-out mirror against the original text. to obtain this goal, it is perfectly necessary to pay attention to semantic different layers, ranged from referential meaning to empirical, interactive, implicit, meanings, semantic uni...

Traditional language parsing is mainly based on generative grammar in English. As English and Chinese belong to two different families of language, a grammar is not sufficient for Chinese parsing although it is still important. In passive sentences in English and Chinese, there exists some similarity, but there also exists some difference. In this paper, first the sememe analysis is introduced ...

Sememes are the smallest semantic units of human languages, composition which can represent meaning words. have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation a word's sememes depends on experts, is both time-consuming and labor-consuming, limiting large-scale application sememe. Researchers proposed some sememe prediction method...

Word semantic similarity is the foundation of semantic processing, and is a key issue in many applications. This paper argues that word semantic similarity should associate with domain knowledge, which traditional methods did not take into account. In order to adopt domain knowledge into semantic similarity measurement, this paper proposed a sensitive words sets approach. For this purpose, we a...

Currently most word sense disambiguation (WSD) systems are relatively individual word sense experts. Scarcely do these systems take word sense transitions between senses of linearly consecutive words or syntactically dependent words into consideration. Word sense transitions are very important. They embody the fluency of semantic expression and avoid sparse data problem effectively. In this pap...

This paper introduces a method which aims at translating Chinese terms into English. Our motivation is providing deep semantic-level information for term translation through analyzing the semantic structure of terms. Using the contextual information in the term and the first sememe of each word in HowNet as features, we trained a Support Vector Machine (SVM) model to identify the dependencies a...

The topic of the paper is the problem how to define case relations by semantic predicates. A general principle is outlined, which renders it possible to "calculate" case relations for a given representation of a (verb-)sememe by means of expressions. This principle is based on an assignment of case relations to primitive predicates and modification rules for nested expressions. Contrary to the ...