Effective Representation for Easy-First Dependency Parsing

Li, Zuchao; Cai, Jiaxun; Zhao, Hai

doi:10.1007/978-3-030-29908-8_28

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…The data used to carry out this step is an Arabic corpus of the Quran taken from http://corpus.quran.com (Dukes & Habash, 2010). The parsing process begins by creating an easy attachment decision to create multiple dependency structures, then it becomes more difficult dependency structures until well-formed dependency trees are created (Li et al, 2019). An input sentence is executed with this parsing.…”

Section: Methodsmentioning

confidence: 99%

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

2020

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Arabic is the main language of Al-Quran. Nowadays, many people are studying the Language of Al-Quran, called Quran Arabic. For the beginners, it is important for them to understand the syntactic relationship in a sentence found in the Qur'an. If they do not understand enough, the interpretation will be different and wrong. It will turn into dangerous because Al-Quran is a source of guidance for Muslims’ life. Dependency parsing is very important for linguistic research, especially for rich languages such as the Arabic Language. This study aims to build dependency parsing, in order to make it easier to get to understand syntactic relationship information in sentences. This study uses a parsing method called deterministic parsing, which the method used is shift-reduce parsing with the Easy-First parsing algorithm. The evaluation used labeled attachment score calculation. The score generated from the evaluation was 69.7, beforehand, the comparison both the system results and the gold standard have been done. 62 sentences found the correct head and relation in each word. The number of words found to be wrong is not more than 3 words in one sentence. Evaluation scores produced are not exorbitant due to the complicated tagset used and lacking test sentences.

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Section: Methodsmentioning

confidence: 99%

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

2020

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“…NLP allows us to use quantitative research methods to study abstract linguistic phenomena. NLP is particularly popular in studeis of syntax and pragmatics [3], e.g., dependency parsing [13], metaphor processing [15], and sentiment analysis [6]. However, to the best of our knowledge, applying these NLP tools to the analysis of the attractiveness of health claims is still new.…”

Section: Related Workmentioning

confidence: 99%

Attractiveness Analysis for Health Claims on Food Packages

Li¹,

Liang²,

Ryder³

et al. 2022

Communications in Computer and Information Science

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Health Claims (Health Claims) on food packages are statements used to describe the relationship between the nutritional content and the health benefits of food products. They are popularly used by food manufacturers to attract consumers and promote their products. How to design and develop NLP tools to better support the food industry to predict the attractiveness of health claims has not yet been investigated. To bridge this gap, we propose a novel NLP task: attractiveness analysis. We collected two datasets: 1) a health claim dataset that contains both EU approved Health Claims and publicly available Health claims from food products sold in supermarkets in EU countries; 2) a consumer preference dataset that contains a large set of health claim pairs with preference labels. Using these data, we propose a novel model focusing on the syntactic and pragmatic features of health claims for consumer preference prediction. The experimental results show the proposed model achieves high prediction accuracy. Beyond the prediction model, as case studies, we proposed and validated three important attractiveness factors: specialised terminology, sentiment, and metaphor. The results suggest that the proposed model can be effectively used for attractiveness analysis. This research contributes to developing an AI-powered decision making support tool for food manufacturers in designing attractive health claims for consumers.

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“…The easy-first parsing approach (Kiperwasser and Goldberg 2016a;Li, Cai, and Zhao 2019) was designed to integrate the advantages of graph-based parsers' betterperforming trees and transition-based parsers' linear decoding complexity. By processing the input tokens in a stepwise easy-to-hard order, the algorithm makes use of structured information on partially-built parse trees.…”

Section: Introductionmentioning

confidence: 99%

Global Greedy Dependency Parsing

Zhang

Parnow

2020

AAAI

Self Cite

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Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacking or re-ranking of partially-built parse trees to build a complete parse tree and are stuck with slower training for the necessity of dynamic oracle training. The latter, graph-based models, may boast better performance but are unfortunately marred by polynomial time inference. In this paper, we propose a novel parsing order objective, resulting in a novel dependency parsing model capable of both global (in sentence scope) feature extraction as in graph models and linear time inference as in transitional models. The proposed global greedy parser only uses two arc-building actions, left and right arcs, for projective parsing. When equipped with two extra non-projective arc-building actions, the proposed parser may also smoothly support non-projective parsing. Using multiple benchmark treebanks, including the Penn Treebank (PTB), the CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our parser and demonstrate that the proposed novel parser achieves good performance with faster training and decoding.

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Effective Representation for Easy-First Dependency Parsing

Cited by 9 publications

References 38 publications

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

Dependency Parsing for Arabic Quran using Easy-First Parsing Algorithm

Attractiveness Analysis for Health Claims on Food Packages

Global Greedy Dependency Parsing

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