The Experts below are selected from a list of 1470 Experts worldwide ranked by ideXlab platform
Yanqiu Shao - One of the best experts on this subject based on the ideXlab platform.
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PACLIC - Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing
2020Co-Authors: Yanqiu Shao, Yu Ding, Lijuan ZhengAbstract:This paper extracts collocationbasing on Semantic Dependency parsing, and then constructs a collocation bankwith two levels according to frequency: the instance-level and Semantic level. Compared with conventional extracting ways, the collocationsextracted in this paper have closer relationship and higher quality both on the lexical structure and Semantic structure.
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SemEval@NAACL-HLT - SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2020Co-Authors: Yanqiu Shao, Yu DingAbstract:This paper describes the SemEval-2016 Shared Task 9: Chinese Semantic Dependency Parsing. We extend the traditional treestructured representation of Chinese sentence to directed acyclic graphs that can capture richer latent Semantics, and the goal of this task is to identify such Semantic structures from a corpus of Chinese sentences. We provide two distinguished corpora in the NEWS domain with 10,068 sentences and the TEXTBOOKS domain with 14,793 sentences respectively. We will first introduce the motivation for this task, and then present the task in detail including data preparation, data format, task evaluation and so on. At last, we briefly describe the submitted systems and analyze these results.
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CCL - Adversarial Domain Adaptation for Chinese Semantic Dependency Graph Parsing
Lecture Notes in Computer Science, 2019Co-Authors: Huayong Li, Zizhuo Shen, Yanqiu ShaoAbstract:The Chinese Semantic Dependency Graph (CSDG) Parsing reveals the deep and fine-grained Semantic relationship of Chinese sentences, and the parsing results have a great help to the downstream NLP tasks. However, most of the existing work focuses on parsing in a single domain. When transferring to other domains, the performance of the parser tends to drop dramatically. And the target domain often lacks the annotated data, so it is difficult to train the parser directly in the target domain. To solve this problem, we propose a lightweight yet effective domain adaptation component for CSDG parsing that can be easily added to the architecture of existing single domain parser. It contains a data sampling module and an adversarial training module. Furthermore, we present CC SD, the first Chinese Cross-domain Semantic graph Dependency dataset. Experiments show that with the domain adaptation component we proposed, the model can effectively improve the performance in the target domain. On the CCSD dataset, our model achieved state-of-the-art performance with significant improvement compared to the strong baseline model.
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NLPCC (1) - Dependency-Gated Cascade Biaffine Network for Chinese Semantic Dependency Graph Parsing
Natural Language Processing and Chinese Computing, 2019Co-Authors: Zizhuo Shen, Huayong Li, Yanqiu ShaoAbstract:The Chinese Semantic Dependency Graph (CSDG) parsing breaks the limitation of the syntactic or Semantic tree structure Dependency system with a richer representation ability to express more complex language phenomena and Semantic relationships. Most of the existing CSDG parsing systems used transition-based approach. It needs to define a complex transition system and its performance depends heavily on whether the model can properly represent the transition state. In this paper, we adopt neural graph-based approach which using Biaffine network to solve the CSDG parsing task. Furthermore, considering that Dependency edge and label have the strong relationship, we design an effective Dependency-gated cascade mechanism to improve the accuracy of Dependency label prediction. We test our system on the SemEval-2016 Task 9 dataset. Experiment result shows that our model achieves state-of-the-art performance with 7.48% and 6.36% labeled F1-score improvement compared to the previous best model in TEXTBOOKS and NEWS domain respectively.
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CLSW - The Annotation Scheme of Sematic Structure Relations Based on Semantic Dependency Graph Bank
Lecture Notes in Computer Science, 2018Co-Authors: Xinghui Cheng, Yanqiu ShaoAbstract: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.
Yu Ding - One of the best experts on this subject based on the ideXlab platform.
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PACLIC - Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing
2020Co-Authors: Yanqiu Shao, Yu Ding, Lijuan ZhengAbstract:This paper extracts collocationbasing on Semantic Dependency parsing, and then constructs a collocation bankwith two levels according to frequency: the instance-level and Semantic level. Compared with conventional extracting ways, the collocationsextracted in this paper have closer relationship and higher quality both on the lexical structure and Semantic structure.
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SemEval@NAACL-HLT - SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2020Co-Authors: Yanqiu Shao, Yu DingAbstract:This paper describes the SemEval-2016 Shared Task 9: Chinese Semantic Dependency Parsing. We extend the traditional treestructured representation of Chinese sentence to directed acyclic graphs that can capture richer latent Semantics, and the goal of this task is to identify such Semantic structures from a corpus of Chinese sentences. We provide two distinguished corpora in the NEWS domain with 10,068 sentences and the TEXTBOOKS domain with 14,793 sentences respectively. We will first introduce the motivation for this task, and then present the task in detail including data preparation, data format, task evaluation and so on. At last, we briefly describe the submitted systems and analyze these results.
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CLSW - Research on Collocation Extraction Based on Syntactic and Semantic Dependency Analysis
Lecture Notes in Computer Science, 2016Co-Authors: Yanqiu Shao, Lijuan Zheng, Yu DingAbstract:In this paper we present a kind of collocation extraction method based on automatic Semantic analysis. On the basis of Semantic Dependency analysis, we use co-occurrence frequency and mutual information to extract collocation. Compared with the accuracy of collocation extraction based on syntactic Dependency parsing, the one based on Semantic Dependency analysis can achieve 77.7% accuracy.
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semeval 2016 task 9 chinese Semantic Dependency parsing
North American Chapter of the Association for Computational Linguistics, 2016Co-Authors: Yanqiu Shao, Yu DingAbstract:This paper describes the SemEval-2016 Shared Task 9: Chinese Semantic Dependency Parsing. We extend the traditional treestructured representation of Chinese sentence to directed acyclic graphs that can capture richer latent Semantics, and the goal of this task is to identify such Semantic structures from a corpus of Chinese sentences. We provide two distinguished corpora in the NEWS domain with 10,068 sentences and the TEXTBOOKS domain with 14,793 sentences respectively. We will first introduce the motivation for this task, and then present the task in detail including data preparation, data format, task evaluation and so on. At last, we briefly describe the submitted systems and analyze these results.
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Dependency graph based chinese Semantic parsing
CCL, 2014Co-Authors: Yu Ding, Yanqiu ShaoAbstract:Semantic Dependency Parsing (SDP) is a deep Semantic analysis task. A well-formed Dependency scheme is the foundation of SDP. In this paper, we refine the HIT Dependency scheme using stronger linguistic theories, yielding a Dependency scheme with more clear hierarchy. To cover Chinese Semantics more comprehensively, we make a break away from the constraints of Dependency trees, and extend to graphs. Moreover, we utilize SVM to parse Semantic Dependency graphs on the basis of parsing of Dependency trees.
Chung-hsien Wu - One of the best experts on this subject based on the ideXlab platform.
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Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model
ACM Transactions on Asian and Low-Resource Language Information Processing, 2019Co-Authors: Ming-hsiang Su, Chung-hsien Wu, Kun-yi HuangAbstract:This article presents an approach to response selection and message-response (MR) database expansion from the unstructured data on the psychological consultation websites for a retrieval-based question answering (QA) system in a constrained domain for emotional support and comforting. First, we manually construct an initial MR database based on the articles collected from the psychological consultation websites. The Chinese Knowledge and Information Processing probabilistic context-free grammar is adopted to obtain the Semantic Dependency graphs (SDGs) of all the messages and responses in the initial MR database. For each sentence in the MR database, all the Semantic dependencies, each composed of two words and their Semantic relation, are extracted from the SDG of the sentence to form a Semantic Dependency set. Finally, a matrix with the element representing the correlation between the Semantic dependencies of the messages and their corresponding responses is constructed as a Semantic Dependency pair model (SDPM) for response selection. Moreover, as the number of MR pairs in the psychological consultation websites is increasing day by day, the MR database in the QA system should be expanded to meet the needs of the users. For MR database expansion, the unstructured data from the message board are automatically collected. For the collected data, the supervised latent Dirichlet allocation is adopted for event detection and then the event-based delta Bayesian Information Criterion is used for message and response article segmentation. Each extracted message segment is then fed to the constructed retrieval-based QA system to find the best matched response segment and the matching score is also estimated to verify if the new MR pair is suitable to be included in the expanded MR database. Fivefold cross validation was employed to evaluate the performance of the proposed retrieval-based QA system over the expanded MR database based on SDPM. Compared to the vector space model-based method, the Okapi BM25 model, and the deep learning-based sequence-to-sequence with attention model, the proposed approach achieved a more favorable performance according to a statistical significance test. The retrieval accuracy based on MR expansion was also evaluated and a satisfactory result was obtained confirming the effectiveness of the expanded MR database. In addition, the user's satisfaction score of the proposed system was evaluated using the Cronbach's alpha value and the satisfaction score of the proposed SDPM was higher than those of the methods for comparison.
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stochastic discourse modeling in spoken dialogue systems using Semantic Dependency graphs
Meeting of the Association for Computational Linguistics, 2006Co-Authors: Chung-hsien Wu, Maozhu YangAbstract:This investigation proposes an approach to modeling the discourse of spoken dialogue using Semantic Dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the Semantic Dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the Semantic Dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.
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ACL - Stochastic Discourse Modeling in Spoken Dialogue Systems Using Semantic Dependency Graphs
Proceedings of the COLING ACL on Main conference poster sessions -, 2006Co-Authors: Chung-hsien Wu, Maozhu YangAbstract:This investigation proposes an approach to modeling the discourse of spoken dialogue using Semantic Dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the Semantic Dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the Semantic Dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.
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Spoken document summarization and retrieval for wireless application
2005 International Conference on Wireless Networks Communications and Mobile Computing, 2005Co-Authors: Chung-hsien Wu, Chien-lin Huang, Chia-hsin HsiehAbstract:For the purpose of wireless data transformation, spoken document summarization can efficiently reduce the redundant contents. This study presents a voice-activated spoken document summarization and retrieval scheme using text and speech analysis. In this method, prosody, speech recognition confidence, word significance, word trigram and Semantic Dependency are considered in the summarization score. A dynamic programming algorithm is used to seek the best summarization result. Experimental results indicate that the proposed approach effectively summarizes concise spoken sentences containing important words with Semantic Dependency.
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ISCSLP - Spoken document summarization using topic-related corpus and Semantic Dependency grammar
SympoTIC '04. Joint 1st Workshop on Mobile Future & Symposium on Trends In Communications (IEEE Cat. No.04EX877), 2004Co-Authors: Chia-hsin Hsieh, Chien-lin Huang, Chung-hsien WuAbstract:The paper presents a spoken document summarization scheme using a topic-related corpus and Semantic Dependency grammar. The summarization score considers speech recognition confidence, word significance, word trigram, Semantic Dependency grammar (SDG) and probabilistic context free grammar (PCFG). In addition, a topic-related corpus consisting of keywords as well as articles is used to estimate the word significance score using latent Semantic indexing (LSI). Semantic relations between words are determined by SDG using HowNet and Sinica Treebank. A dynamic programming algorithm is applied to decide the summarization ratio and look for the best summarization result according to summarization scores. Experimental results indicate that the proposed approach effectively extracts important words with Semantic Dependency and gives a promising speech summary.
Maozhu Yang - One of the best experts on this subject based on the ideXlab platform.
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stochastic discourse modeling in spoken dialogue systems using Semantic Dependency graphs
Meeting of the Association for Computational Linguistics, 2006Co-Authors: Chung-hsien Wu, Maozhu YangAbstract:This investigation proposes an approach to modeling the discourse of spoken dialogue using Semantic Dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the Semantic Dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the Semantic Dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.
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ACL - Stochastic Discourse Modeling in Spoken Dialogue Systems Using Semantic Dependency Graphs
Proceedings of the COLING ACL on Main conference poster sessions -, 2006Co-Authors: Chung-hsien Wu, Maozhu YangAbstract:This investigation proposes an approach to modeling the discourse of spoken dialogue using Semantic Dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the Semantic Dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the Semantic Dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.
Stephan Oepen - One of the best experts on this subject based on the ideXlab platform.
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Broad-Coverage Semantic Dependency Parsing
2020Co-Authors: Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Jan Haji, Angelina Ivanova, Yi ZhangAbstract:Task 8 at SemEval 2014 defines BroadCoverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate‐argument relationships for all content words, i.e. the Semantic structure constituting the relational core of sentence meaning. In this task description, we position the problem in comparison to other sub-tasks in computational language analysis, introduce the Semantic Dependency target representations used, reflect on high-level commonalities and differences between these representations, and summarize the task setup, participating systems, and main results.
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Semantic Dependency graph parsing using tree approximations
Proceedings of the 11th International Conference on Computational Semantics, 2015Co-Authors: żeljko Agic, Alexander Koller, Stephan OepenAbstract:In this contribution, we deal with graph parsing, i.e., mapping input strings to graph-structured output representations, using tree approximations. We experiment with the data from the SemEval 2014 Semantic Dependency Parsing (SDP) task. We define various tree approximation schemes for graphs, and make twofold use of them. First, we statically analyze the Semantic Dependency graphs, seeking to unscover which linguistic phenomena in particular require the additional annotation expressivity provided by moving from trees to graphs. We focus on undirected base cycles in the SDP graphs, and discover strong connections to grammatical control and coordination. Second, we make use of the approximations in a statistical parsing scenario. In it, we convert the training set graphs to Dependency trees, and use the resulting treebanks to build standard Dependency tree parsers. We perform lossy graph reconstructions on parser outputs, and evaluate our models as Dependency graph parsers. Our system outperforms the baselines by a large margin, and evaluates as the best non-voting tree approximation–based parser on the SemEval 2014 data, scoring at just over 81% in labeled F1.
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IWCS - Semantic Dependency Graph Parsing Using Tree Approximations
2015Co-Authors: Żeljko Agić, Alexander Koller, Stephan OepenAbstract:In this contribution, we deal with graph parsing, i.e., mapping input strings to graph-structured output representations, using tree approximations. We experiment with the data from the SemEval 2014 Semantic Dependency Parsing (SDP) task. We define various tree approximation schemes for graphs, and make twofold use of them. First, we statically analyze the Semantic Dependency graphs, seeking to unscover which linguistic phenomena in particular require the additional annotation expressivity provided by moving from trees to graphs. We focus on undirected base cycles in the SDP graphs, and discover strong connections to grammatical control and coordination. Second, we make use of the approximations in a statistical parsing scenario. In it, we convert the training set graphs to Dependency trees, and use the resulting treebanks to build standard Dependency tree parsers. We perform lossy graph reconstructions on parser outputs, and evaluate our models as Dependency graph parsers. Our system outperforms the baselines by a large margin, and evaluates as the best non-voting tree approximation–based parser on the SemEval 2014 data, scoring at just over 81% in labeled F1.
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semeval 2015 task 18 broad coverage Semantic Dependency parsing
North American Chapter of the Association for Computational Linguistics, 2014Co-Authors: Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Silvie Cinkova, Jan Hajic, Zdenka UresovaAbstract:Task 18 at SemEval 2015 defines Broad-Coverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate–argument relationships for all content words, i.e. the sema ...
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SemEval@NAACL-HLT - SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014Co-Authors: Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Silvie Cinkova, Jan Hajic, Zdenka UresovaAbstract:Task 18 at SemEval 2015 defines Broad-Coverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate–argument relationships for all content words, i.e. the sema ...