《现代汉语动词语义知识词典》的开发与应用

该文吸收已有动词研究的相关成果,提出了动词语义词典开发的相关原则和研制思路,界定并描写了词典中所涉及的相关属性信息,并对词典的总体文件结构及其各个库的信息进行了描写和说明。最终开发了融合词汇语义和句法语义,涵盖词形、词性、释义、义类、义场、句法范畴信息、语义范畴信息、语义句模等多种信息参数的开放性的动词语义知识词典。该词典可以在歧义分化、词义关系考察、句法—语义接口、句模抽取等方面提供支持。     

Determining Prepositional Attachment, Prepositional Meaning, Verb Meaning, and Thematic Roles

An algorithm for semantic interpretation that integrates the determination of the meaning of verbs, the attachment and meaning of prepositions, and the determination of thematic roles is presented. The parser does not resolve structural ambiguity, which is solely the task of the semantic interpreter. Lexical semantic information about nouns and verbs is applied to the resolution of verb polysemy and modifier attachment. Semantic interpretation is centered on the representation of the meaning of the verb, called verbal concept. Verbal concepts are organized into a classification hierarchy. As long as the meaning of the verb remains unknown, parsing proceeds on a syntactic basis. Once the meaning of the verb is recognized, the semantic component makes sense of the syntactic relations built so far by the parser and of those still to be parsed. The algorithm has been implemented and tested on real–world texts.     

基于概念知识树的双宾短语分析

双宾短语是一种特殊的语言现象,为了使计算机能够理解并处理双宾短语,该文从语法和语义两个层面对双宾短语进行了分析,基于概念知识树知识表示模型建立了双宾短语的语义表达模型;并提出一种双宾短语分析算法,实现了从双宾短语到其语义表达模型的自动转换。双宾短语分析算法采用自顶向下和自底向上相结合的方法,自顶向下用于对双宾短语的语法成分进行划分,获得构成双宾短语的双宾动词成分、间接宾语成分和直接宾语成分;自底向上用于使用基于概念知识树的短语分析推理算法对双宾短语中的这三种成分分别进行分析,获得对应的语义表达;最后,利用三种成分的语义分析结果构建双宾短语完整的语义表达。该文从权威文献和语法词典中选取了122个双宾动词,对这些双宾动词构成的209个短语进行了分析,分析的正确率为90.43%,证明了该文提出的双宾短语分析算法和语义表达模型的有效性。     

用动词的论元结构跟事件模板相匹配——一种由动词驱动的信息抽取方法

本文以文献[2 ]中信息抽取模型( InfoX) 的测试语料(职务变动文本) 为主要对象,具体说明怎样建立从动词的论元结构到相关的事件模板的匹配关系。首先根据职务变更动词的有关句法、语义特点,把它分成六个小类:任命、担任、免职、辞职、调遣、受命;然后,分别描写每一小类动词的论元结构,特别是它们所支配的论元角色及其句法配置方式。最后,建立动词的论元角色跟事件模板元素的匹配关系,并揭示动词对文本筛选和合并都有导向作用,说明发展由动词驱动的信息抽取方法的可行性。     

Investigating the cross-lingual translatability of VerbNet-style classification

VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.     

基于动词论元结构的中文事件抽取方法

为将动词与其论元间的约束规则应用于事件抽取,在事件模型中引入动词论元结构形成模型变体,围绕模型变体提出基于动词论元结构的中文事件抽取方法。此方法首先对待抽取文本进行预处理和句法分析,得出其语法结构;然后将所得结构与动词论元结构属性进行比较,找出每个动词支配的论元;最后利用论元的语义属性确定与之对应的事件特征并由此完成事件抽取。实验结果显示,此方法能有效提高抽取系统的性能和效率。     

Representing natural language with Prolog

The authors describe the use of Prolog to build a syntactic list structure and a syntactic-semantic structure and to incorporate the semantic structure into a background structure that conveys the meanings of the individual words in the sentence in the context of general work knowledge. This semantic structure is then placed in the context of the sentence being parsed. Rather than rely solely on case grammar to represent the functions of words in a sentence, the authors have extended this technique to include a frame structure (which gives semantic details of the verb and the relations of other words to it) for a sentence's verbs to build the syntactic-semantic structure. Using this frame structure conveys more information than using only the case-grammar approach. The authors then place the syntactic-semantic structure in the context of its background knowledge, using the principles of partitioned networks in which word meanings are placed in a hierarchical structure that represents the background knowledge.<>     

Multilingual generation: The role of telicity in lexical choice and syntactic realization

Multilingual generation in machine translation (MT) requires a knowledge organization that facilitates the task of lexical choice, i.e. selection of lexical units to be used in the generation of a target-language sentence. This paper investigates the extent to which lexicalization patterns involving the lexical aspect feature [+telic] may be used for translating events and states among languages. Telicity has been correlated syntactically with both transitivity and unaccusativity, and semantically with Talmy's path of a motion event, the representation of which characterizes languages parametrically.Taking as our starting point the syntactic/semantic classification in Levin's English Verb Classes and Alternations, we examine the relation between telicity and the syntactic contexts, or alternations, outlined in this work, identifying systematic relations between the lexical aspect features and the semantic components that potentiate these alternations. Representing lexical aspect — particularly telicity — is therefore crucial for the tasks of lexical choice and syntactic realization. Having enriched the data in Levin (by correlating the syntactic alternations (Part I) and semantic verb classes (Part II) and marking them for telicity) we assign to verbs lexical semantic templates (LSTs). We then demonstrate that it is possible from these templates to build a large-scale repository for lexical conceptual structures which encode meaning components that correspond to different values of the telicity feature. The LST framework preserves both semantic content and semantic structure (following Grimshaw during the processes of lexical choice and syntactic realization. Application of this model identifies precisely where the Knowledge Representation component may profitably augment our rules of composition, to identify cases where the interlingua underlying the source language sentence must be either reduced or modified in order to produce an appropriate target language sentence.     

基于次范畴化的汉语多义动词模糊聚类

描述了应用模糊k均值方法聚类汉语多义动词的实验,共涉及到60个汉语动词,40个多义词,20个单义词.首先,自动获取每个动词的次范畴化框架的概率分布,然后,导出这些动词的模糊聚类.结果表明,纯洁度和对精确度的综合量度较好地反映了聚类性能,尽管动词的句法行为在一定程度上体现了深层语义,但汉语动词的句法行为不易从单一的语义层预测出来.     

基于子类的汉语动词SCF获取回退

研究证明基于动词子类的回退方法能够改进次范畴化(SCF)自动获取的性能,不同的子类划分方法起到重要的作用。本文利用《同义词词林》、《知网1.2版》和《现代汉语动词大词典》分别对30个测试动词进行分类,并以分类结果来改进汉语动词SCF自动获取的性能,实验结果证明:一、回退后的获取性能有了较为明显的提高;二、涉及句法成分较多的分类方法更适合汉语SCF自动获取;三、随机组合相关动词来构造回退子类是不合理的;四、上述分类都不是适合汉语动词SCF获取回退的最优方法。     

现代汉语常用动词释义对比研究——以《现代汉语词典》(第六版)和《重編國語辭典修訂本》为例

动词在语言中的地位十分重要,而汉语更是一种动词性语言。了解动词的释义,是研究动词的一个重要途径。该文采用《现代汉语词典》(第六版)和《重編國語辭典修訂本》为对比研究的材料,参考新汉语水平考试大纲,选取三者共有的动词词条进行研究,着重对比两本词典对动词的释义,找出普通话和台湾地区所用标准语两者在动词方面的差异,减少两岸交流中因词义不同所产生的误会,更好地促进两岸交流。     

Peer-to-peer semantic coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web. In this paper, we propose a method for discovering semantic mappings across hierarchical classifications (HCs) based on a new approach, which shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different schema. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.     

From syntactic encodings to thematic roles: Building lexical entries for interlingual MT

Our goal is to construct large-scale lexicons for interlingual MT of English, Arabic, Korean, and Spanish. We describe techniques that predict salient linguistic features of a non-English word using the features of its English gloss (i.e., translation) in a bilingual dictionary. While not exact, owing to inexact glosses and language-to-language variations, these techniques can augment an existing dictionary with reasonable accuracy, thus saving significant time. We have conducted two experiments that demonstrate the value of these techniques. The first tested the feasibility of building a database of thematic grids for over 6500 Arabic verbs based on a mapping between English glosses and the syntactic codes in Longman's Dictionary of Contemporary English (LDOCE) (Procter, 1978). We show that it is more efficient and less error-prone to hand-verify the automatically constructed grids than it would be to build the thematic grids by hand from scratch. The second experiment tested the automatic classification of verbs into a richer semantic typology based on (Levin, 1993), from which we can derive a more refined set of thematic grids. In this second experiment, we show that a brute-force, non-robust technique provides 72% accuracy for semantic classification of LDOCE verbs; we then show that it is possible to approach this yield with a more robust technique based on fine-tuned statistical correlations. We further suggest the possibility of raising this yield by taking into account linguistic factors such as polysemy and positive and negative constraints on the syntax-semantics relation. We conclude that, while human intervention will always be necessary for the construction of a semantic classification from LDOCE, such intervention is significantly minimized as more knowledge about the syntax-semantics relation is introduced.     

现代藏语动词的句法语义分类及相关语法句式

本文突破了传统藏文文法关于动词分类的简单描述,建立起以句法语义为纲要的动词类别和相关句法规则。本文区分了藏语12大类动词,各类动词都有不同论元数量和不同句法性质的要求。因此,动词的句法语义类别划分能够较细致和全面反映各种类型藏语句式的语法结构框架,包括句子的语序、词格标记和句法助词。动词的句法语义分类结果可以直接应用于藏语语法信息词典的构建,是藏语计算处理的重要基础。     

Polysemy Deciphering Network for Robust Human–Object Interaction Detection

Human–Object Interaction (HOI) detection is important to human-centric scene understanding tasks. Existing works tend to assume that the same verb has similar visual characteristics in different HOI categories, an approach that ignores the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net) that decodes the visual polysemy of verbs for HOI detection in three distinct ways. First, we refine features for HOI detection to be polysemy-aware through the use of two novel modules: namely, Language Prior-guided Channel Attention (LPCA) and Language Prior-based Feature Augmentation (LPFA). LPCA highlights important elements in human and object appearance features for each HOI category to be identified; moreover, LPFA augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce intra-class variation for the same verb. Second, we introduce a novel Polysemy-Aware Modal Fusion module, which guides PD-Net to make decisions based on feature types deemed more important according to the language priors. Third, we propose to relieve the verb polysemy problem through sharing verb classifiers for semantically similar HOI categories. Furthermore, to expedite research on the verb polysemy problem, we build a new benchmark dataset named HOI-VerbPolysemy (HOI-VP), which includes common verbs (predicates) that have diverse semantic meanings in the real world. Finally, through deciphering the visual polysemy of verbs, our approach is demonstrated to outperform state-of-the-art methods by significant margins on the HICO-DET, V-COCO, and HOI-VP databases. Code and data in this paper are available at https://github.com/MuchHair/PD-Net.

     

施事的语义分布考察与动词的语义特征

在自然语言处理中,语义角色标注是一个比较重要的任务。语义角色标注任务以语义角色理论为基础。语义角色标注任务和语言学的相关理论都要求同一个动词在不同语句中相应的语义角色成分保持一致。该文统计了北京大学973课题语义角色标注语料库及中文PropBank中“施事”的语义分布,进而根据该语义分布,加上人工筛选,共找出了85个在“施事”语义角色的标注上无法保持一致的动词。该文指出,这些动词的 “施事”语义无法保持一致的原因在于它们是一类有致使语义特征、无自主语义特征的动词。该文的研究对于处理其他语义角色(如“客体”)也存在的标注不一致问题有一定的启示。     

Computational verb systems: Verb logic

Computational verb systems are new platforms for artificial intelligence. They embed the dynamical knowledge expressed by dynamical experiences of human brains into machines by using pattern thinking. In this paper, the relationship defined by verbs in human natural languages is modeled by computational verb logic (verb logic for short). To unify different verbs with different contexts into comparable standard forms, the concepts of BE transformation and canonical BE transformation are given. The atomic and molecular verb sentences under canonical BE transformations are also defined for verb logic. The basic verb logic operations are given. Some examples are given to demonstrate the concepts of BE transformation and verb logic. ©1999 John Wiley & Sons, Inc.     

The availability of verb–particle constructions in lexical resources: How much is enough?

In this paper, we investigate the phenomenon of verb–particle constructions, discussing their characteristics and availability in some lexical resources. Given the limited coverage provided by these resources and the constantly growing number of verb–particle combinations, possible ways of extending their coverage are investigated, taking into account regular patterns found in some productive combinations of verbs and particles. We propose, in particular, the use of a semantic classification of verbs (such as that defined by [English verb classes and alternations – a preliminary investigation, The University of Chicago Press]) as a means to obtain productive verb–particle constructions and the use of the World Wide Web to validate them, and discuss the issues involved in adopting such an approach.