Personal Attributes Extraction Based on the Combination of Trigger Words, Dictionary and Rules

Zhang, Kailun; Wang, Mingyin; Cong, Xiaoyue; Huang, Fang; Xue, Hongfa; Li, Lei; Gao, Zhiqiao

doi:10.3115/v1/w14-6818

Cited by 8 publications

(5 citation statements)

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“…Kailun Zhang et al [7] propose a method based on the combination of trigger words, dictionary and rules. This system narrow down the extraction scope by building attributes trigger words.…”

Section: Figure 3 the Strict Evaluation Resultsmentioning

confidence: 99%

Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014: Overview

Xu¹,

Feng²,

Xu³

2014

Proceedings of the Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

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This paper presents the overview of Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014. Personal attribute extraction plays an important role in information extraction, event tracking, entity disambiguation and other related research areas. This task is designed to evaluate the techniques for extracting person specific attributes from unstructured Chinese texts, which is similar to slot filling, but focuses on person attributes. This task brings some challenges issues because Chinese language contains some common words and lacks of capital clues as in English. The task organizer manually constructs the query names and corresponding documents. The value/presence of the texts corresponding 25 pre-defined attributes are annotated to construct the training and testing dataset. The bakeoff results achieved by the participators show the good progress in this field.

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“…Kailun Zhang et al [7] propose a method based on the combination of trigger words, dictionary and rules. This system narrow down the extraction scope by building attributes trigger words.…”

Section: Figure 3 the Strict Evaluation Resultsmentioning

confidence: 99%

Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014: Overview

Xu¹,

Feng²,

Xu³

2014

Proceedings of the Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

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“…The major approaches of NER for Chinese historical texts have been focused on handcrafted heuristic rules [6][7][8][9][10][11] to formulate entity features from context-derived patterns in a quick manner, which depend a lot on relevant domain knowledge. For example, MARKUS 3 [12] is a well-known online platform for automatically tagging a range of historical named entities (personal names, place names and bureaucratic offices, etc.)…”

Section: A Entity Extraction Of Chinese Historical Textsmentioning

confidence: 99%

“…For robust evaluation, we train our model for 5 times with 100 epochs and obtain the average evaluative indicators defined in the part D of Section IV. The model has been implemented through Keras 10 and Tensorflow 11 API. The whole experiment is run on the Google Colab platform 12 with a Tesla P100 GPU.…”

Section: Training Setupmentioning

confidence: 99%

MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Yan

Su²,

Wang³

2020

IEEE Access

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Named Entity Recognition (NER) systems have been largely advanced by deep neural networks in the recent decade. However, the state-of-the-arts on NER have been less applied to Chinese historical texts due to the lack of standard corpora in Chinese historical domains and the difficulty of accessing a quality ancient corpus. This paper addresses the respective issues and proposes an efficient automatic processing solution for tackling NER of ancient Chinese data, including the implementation of data-driven tagging and an innovative end-to-end network namely "MoGCN" (Mixture of Gated Convolutional Neural Network). A corpus consisting of three genres of Chinese historical classics is generated by our tagging approach, which is experimented for uncovering the generalization ability of proposed model. The empirical analysis demonstrates that our proposed model achieves the best results with above 1.5% F1-score improvement over other sophisticated models in this dataset, where the experimental performance shows positive dependence on the quality of corpus. Furthermore, our model can perform much better on shorter entities especially for 2-charater ones, while many long-range entities can be only identified by our model based on our auxiliary attribute analysis. This work serves as a preliminary exploitation of NER for historical data, providing unique insights and reference values for similar tasks. Future work should be focused on more exploration about NER optimization on massive Chinese traditional texts with linguistic features and learning strategies. INDEX TERMS Named entity recognition, gated neural network, Chinese historical texts

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“…Such text-based techniques are limited in the scalability in large code bases and only finding exact match code clone pairs. Second, tree or token-based clone detections [26], [37], [9], [50] are proposed by parsing program into tokens or generate abstract syntax trees representation of the source program. However, above approaches are still not sufficient to detect semantics-similar code clones.…”

Section: Bug Findingsmentioning

confidence: 99%

Twin-Finder: Integrated Reasoning Engine for Pointer-Related Code Clone Detection

Xue

Mei

Gogineni

et al. 2020

2020 IEEE 14th International Workshop on Software Clones (IWSC)

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Detecting similar code fragments, usually referred to as code clones, is an important task. In particular, code clone detection can have significant uses in the context of vulnerability discovery, refactoring and plagiarism detection. However, false positives are inevitable and always require manual reviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach for pointerrelated code clone detection that integrates machine learning and symbolic execution techniques to achieve precision. Twin-Finder+ introduces a formal verification mechanism to automate such manual reviews process. Our experimental results show Twin-Finder+ that can remove 91.69% false positives in average. We further conduct security analysis for memory safety using realworld applications, Links version 2.14 and libreOffice-6.0.0.1. Twin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one public patched bug in libreOffice-6.0.0.1.

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Personal Attributes Extraction Based on the Combination of Trigger Words, Dictionary and Rules

Cited by 8 publications

References 0 publications

Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014: Overview

Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014: Overview

MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Twin-Finder: Integrated Reasoning Engine for Pointer-Related Code Clone Detection

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