A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Lv, Chengyao; Pan, Deng; Li, Yaxiong; Li, Jianxin; Zong, Wang

doi:10.1155/2021/6610965

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…In 2000, McCallum [18] applied a combination model of the maximum entropy model and the Markov model (MEMM) to solve the information extraction and segmentation tasks. Subsequently, the MEMM is applied in the semantic role labeling [19], human activity recognition using a depth camera [20], Chinese entity extraction [21], and other fields.…”

Section: Related Workmentioning

confidence: 99%

An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis

Xiong

Fan

Batra

et al. 2023

Entropy

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Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. However, there are very few published datasets for CTAS. This paper introduces a new benchmark dataset for the task of CTAS to promote development in this research direction. Specifically, our benchmark is a CTAS dataset with the following advantages: (a) it is Weibo-based, which is the most popular Chinese social media platform used by the public to express their opinions; (b) it includes the most comprehensive affective structure labels at present; and (c) we propose a maximum entropy Markov model that incorporates neural network features and experimentally demonstrate that it outperforms the two baseline models.

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Section: Related Workmentioning

confidence: 99%

An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis

Xiong

Fan

Batra

et al. 2023

Entropy

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“…After the preprocessing stage, the data on the corpus is processed using the MEMM feature which is used to make contextual predictions. The MEMM is the most common form of classification of maximum entropy [23], [24]. Maximum entropy is defined as the average maximum information value for a set of events X with a uniform probability value distribution [25].…”

Section: Implementation Of the Maximum Entropy Markov Model Algorithmmentioning

confidence: 99%

Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

Cahyani

Mustikaningtyas

2022

IJ-AI

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This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.

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“…For example, A and B are good friends, and friends are the relationship between them. Lv et al proposed an entity relation extraction model based on bidirectional maximum entropy [21]. Specifically, it first takes the triple as the entity relationship chain to identify the entity before the relationship and predict its corresponding relationship and entity.…”

Section: Data Representation In Databasementioning

confidence: 99%

Hyper-Class Representation of Data

Zhang,

Li,

Zhang

et al. 2022

Preprint

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Data representation is often of the natural form with their attribute values. To utilize the data efficiently, one needs to well understand the observed attribute values and identify the potential useful information in the data/smaples, or training data. In this paper, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. At first, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising.

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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Cited by 13 publications

References 19 publications

An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis

An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis

Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

Hyper-Class Representation of Data

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