检索结果-内蒙古大学图书馆

Relation extraction based on semantic dependency graph

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2020年第1期20卷 279-290页

作者： Jiang, Ming He, Jiecheng Wu, Jianping Qi, Chengjie Zhang, Min Hangzhou Dianzi Univ Hangzhou Zhejiang Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

Distant supervision has a good effect in relation extraction tasks. Meanwhile, most methods use multi-instance learning to reduce the impact of training data been wrong labelled in distant supervision. However, the effect of multi-instance learning depends on the sentence feature vector extracted by the neural network. At present, most methods for extracting sentence features only pay attention to the structural features of sentences, while ignoring semantic features. As a result, structural feature sentences and semantic feature sentences cannot occupy the same proportion in multi-instance learning, which further influences the precision of the model. To alleviate this issue, we propose a BiLSTM-CNN-Attention model (BLCANN) based on semantic dependency graph to extract sentence features. In this model, we extract the shortest dependency path between the two entities from the semantic dependency graph as the input to the model. The shortest path combines the structural and semantic features of the sentence, which contributes to distinguishing between positive and negative examples in multiinstance learning. Experimental results show that our model is adept in extracting structural features and semantic features. Our model has increased the precision of the relationship extraction on Top100 by 10 percent compared to the baseline [9].

关键词： Relation extraction multi-instance learning structural feature semantic feature BiLSTM-CNN-Attention semantic dependency graph shortest path

来源：评论

学校读者我要写书评

暂无评论

Study on Chinese Discourse semantic Annotation Based on semantic dependency graph 19th

Study on Chinese Discourse Semantic Annotation Based on Sema...

引用

19th Chinese Lexical semantics Workshop (CLSW)

作者： Chen, Bo Lyu, Chen Ji, Ziqing Guangdong Univ Foreign Studies Collaborat Innovat Ctr Language Res & Serv Guangzhou 510420 Peoples R China Univ Maryland Coll Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9783030040154;9783030040147

semantic annotation of discourse is always one of most important tasks in natural language processing (NLP). We proposed a complex semantic representation mechanism for Chinese complex sentences. The semantic relations between sentences and between words can be represented as a semantic dependency graph by recursive directed graph. We studied the basic definition, the types of relations, and dependency direction of semantic dependence, and discussed the formal representation mechanism of semantic dependency from phrase-level, sentence-level and discourse-level. The semantic dependency graph model is characterized by allowing multiple correlations, allowing recursion and nesting, and formal representations are shown in recursive directed graph. The semantic dependency graph can more comprehensively represent the semantic relations between words and between the clauses in the discourse.

关键词： semantic dependency graph Discourse semantic annotation Recursion

来源：评论

学校读者我要写书评

暂无评论

The Annotation Scheme of Sematic Structure Relations Based on semantic dependency graph Bank 19th

The Annotation Scheme of Sematic Structure Relations Based o...

引用

19th Chinese Lexical semantics Workshop (CLSW)

作者： Cheng, Xinghui Shan, Yanqiu Beijing Language & Culture Univ Informat Sci Sch Beijing 10083 Peoples R China

ISBN: (纸本)9783030040154;9783030040147

semantic dependency graph is a kind of deep semantic analysis method. In order to annotate every component of a sentence, we proposed an annotation scheme including three different types of semantic relations. They are semantic roles, semantic structure relations and semantic marks. Specifically semantic structure relations deal with the situations in which a sentence contains more than one verbal concepts. This paper will focuses on the three sub-structures of semantic structure relations-Reverse Relations, Nested Relations and Event Relations.

关键词： semantic dependency graph Annotation scheme Structure relations Statistics

来源：评论

学校读者我要写书评

暂无评论

Evaluation and Analysis of the Chinese semantic dependency Understanding Ability of Large Language Models 13th

Evaluation and Analysis of the Chinese Semantic Dependency U...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Zizhuo Li, Wei Shao, Yanqiu Beijing Language & Culture Univ Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

semantic dependency graph is a framework for representing deep semantic knowledge through flexible graph structures. While recent works indicate that large language models (LLMs) have impressive language and knowledge understanding abilities, it remains unclear whether they can understand this deep semantic knowledge. To explore this problem, we design four prompt-style probing tasks from aspects of semantic structure and semantic relations to adapt the inherent abilities of LLMs. To ensure thorough evaluation, we conduct extensive experiments in both in-context learning (ICL) and supervised fine-tuning (SFT) scenarios. Our findings indicate that the understanding of deep semantic knowledge requires larger parameter scale, especially the understanding of high-order semantic structure knowledge and semantic relation knowledge. Furthermore, our experiments reveal that while LLMs perform well on the in-domain (ID) test set via SFT, their generalization ability on out-of-domain (OOD) test set remains inadequate.

关键词： semantic dependency graph Large Language Model Evaluation

来源：评论

学校读者我要写书评

暂无评论

Event Detection Based on Multilingual Information Enhanced Syntactic dependency GCN 15th

Event Detection Based on Multilingual Information Enhanced S...

引用

15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Wang, Zechen Li, Binbin Wang, Yong Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9783031109898;9783031109881

Event detection is a hot and difficult problem in information extraction. It is widely used in automatic news extraction, financial event analysis and other fields. However, most of the existing event detection methods only focus on a single language, ignoring the event information provided by other languages, and can not solve the problem of polysemy in a single language, which makes it difficult to improve the performance of event detection methods. To solve these problems, this paper proposes a newEvent Detection based on Multilingual InformationEnhanced Syntactic dependency GCN. Specifically, the model translates the original language and aligns words, takes multilingual data as input, and constructs syntactic dependency diagrams for initial language sentences. Then, a graph neural network is constructed based on the syntactic dependency graph, and combined with the attention mechanism, the nodes of the syntactic dependency graph are enhanced by the translated language. Finally, the classifier finds the trigger and judges the event type. The model effectively improves the recognition efficiency of polysemous words by using multilingual information, andmakes full use of sentence structure information by using syntactic dependency graph. Experiments on ace2005 benchmark data set showthat the model can detect events effectively and is obviously superior to the existing event detection methods.

关键词： Event detection Multilingual information semantic dependency graph GCN Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Construction of Chinese semantic Annotation Resource of Connective Structures 19th

Construction of Chinese Semantic Annotation Resource of Conn...

引用

19th Chinese Lexical semantics Workshop (CLSW)

作者： Chen, Bo Lyu, Chen Ji, Ziqing Guangdong Univ Foreign Studies Collaborat Innovat Ctr Language Res & Serv Guangzhou 510420 Peoples R China Univ Maryland Coll Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9783030040154;9783030040147

This paper aims to study Chinese connective structures for semantic parsing based on the semantic dependency graph we proposed. The resources we built include three parts: first, Chinese connectives ontology that contains 1291 words;second, a large-scale Chinese connective structure semantic annotation resource with 20,000 sentences;third, a pattern-set of Chinese connective structure that facilitates the next step-automatic analysis. We use manually annotation to ensure the accuracy of the resources. We also make an annotation criteria handbook.

关键词： Connective structure semantic dependency graph semantic annotation Resource construction

来源：评论

学校读者我要写书评

暂无评论

Speech Act Identification Using semantic dependency graphs with Probabilistic Context-Free Grammars

引用

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2016年第1期15卷 1–28页

作者： Yeh, Jui-Feng Natl Chiayi Univ 300 Syuefu Rd Chiayi 60004 Taiwan

We propose an approach for identifying the speech acts of speakers' utterances in conversational spoken dialogue that involves using semantic dependency graphs with probabilistic context-free grammars (PCFGs). The semantic dependency graph based on the HowNet knowledge base is adopted to model the relationships between words in an utterance parsed by PCFG. dependency relationships between words within the utterance are extracted by decomposing the semantic dependency graph according to predefined events. The corresponding values of semantic slots are subsequently extracted from the speaker's utterances according to the corresponding identified speech act. The experimental results obtained when using the proposed approach indicated that the accuracy rates of speech act detection and task completion were 95.6% and 77.4% for human-generated transcription (REF) and speech-to-text recognition output (STT), respectively, and the average numbers of turns of each dialogue were 8.3 and 11.8 for REF and STT, respectively. Compared with Bayes classifier, partial pattern tree, and Bayesian-network-based approaches, we obtained 14.1%, 9.2%, and 3% improvements in the accuracy of speech act identification, respectively.

关键词： Spoken language processing conversational dialogue systems speech act identification semantic dependency graph probabilistic context-free grammars

来源：评论

学校读者我要写书评

暂无评论

semantic-based Software clustering using hill climbing 18

Semantic-based Software clustering using hill climbing

引用

18th CSI International Symposium on Computer Science and Software Engineering Conference (CSSE)

作者： Kargar, Masoud Isazadeh, Ayaz Izadkhah, Habib Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Univ Tabriz Dept Comp Sci Tabriz Iran

ISBN: (纸本)9781538613023

Clustering techniques are used for extracting software architecture in reverse engineering process. Extracting the Call dependency graph (CDG) from the source code is the first step in the process of software clustering. A CDG indicates the method invocations between software's artifacts. This graph is tightly coupled to the used programming language so that the existing toolsets for constructing a CDG works on the particular programming language. Therefore, using existing CDG extraction toolsets for large-scale software systems, e.g., Mozilla Firefox, which written by different programming languages, is impossible. To overcome this problem, in this paper, we propose a new dependency graph, called semantic dependency graph (SDG), which is independent of the programming languages. The combination of lexical analysis and latent semantic analysis (LSA) generates this graph. Two versions of Hill climbing algorithms are used to compare their performance with SDG and CDG. The results show that the SDG can be replaced instead of CDG. The results of various experiments confirm this claim. The SDG can be independent of programming languages, hence, helps to the software engineer to clustering the large-scale software systems to extract software architecture, aiming to understand and maintain the existing software systems.

关键词： software clustering semantic dependency graph call dependency graph Hill Climbing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：