Improving Persian Dependency-Based Semantic Role Labeling using Semantic and structural Relations

Lazemi, Soghra; Ebrahimpour-Komleh, Hossein; Noroozi, Nasser

doi:10.1109/pria.2019.8785990

Cited by 2 publications

(8 citation statements)

References 11 publications

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“…Text Formality: Formal News A combination of many specific topics English [32], [33], [34], [35], [36], [37], [38], [39], [40]; Catalan, Czech, Spanish [35]; German [35], [37]; Italian [41]; Sinhala [42]; Indonesian [23], [43], [44]; Chinese [35], [45], [46], [47], [37], [39]; Myanmar [48]; Russian [49]; Amharic [50]; Arabic [51], [39]; Persian [52]; Portugese [53]; Vietnamese [54];…”

Section: Languages and Selected Studiesmentioning

confidence: 99%

“…The lack of recent machine learning-based SRL research may be due to the need to identify several features before predicting semantic role labels, which can lead to propagation issues [3]. Lazemi et al [52] used the Maximum Entropy (MaxEnt) classifier to predict semantic role labels from Persian language news data. MaxEnt employs the maximum entropy principle to predict semantic role based on the highest significant entropy value [4].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…Therefore, position features can be useful in exceptional cases, such as when subject-object-verb order is to be ignored. The voice feature indicates whether the verb is in active or passive form because the structure of the verb can determine the presence or absence of specific arguments [52].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…Selected Studies Precision, recall, F1 score [112], [38], [37], [56], [54], [53], [39], [28], [59], [77], [65], [66], [47], [36], [49], [74], [50], [22], [59], [52], [55], [21], [35], [113], [32], [68], [62], [41], [40], [60], [33], [81], [57], [76], [46], [71], [44], [48] Alignment with gold standard [114], [112], [22], [60], [76], [38]…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…For example, Amharic became the official language used by the Ethiopian government, which shows the typical behavior of Semitic languages (a group of languages in the Middle East and North Africa) [50]. This diversity of languages gives rise to many different sentence structures for each language, for example, in Persian and Amharic, which have free sentence structures, allowing for various word orders, including Verb-Subject-Object (VSO), Object-Verb-Subject (OVS), Subject-Verb-Object (SVO) [52]. This flexibility in word order makes it difficult for the SRL model to identify arguments and semantic roles in sentences, especially when there are arguments in the form of proper nouns and do not have explicit grammatical case markers.…”

Section: ) Language Issuesmentioning

confidence: 99%

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A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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Challenges in the big data phenomenon arise due to the existence of unstructured text data, which is very large, comes from various sources, has various formats, and contains much noise. The complexity of unstructured text data makes it difficult to extract useful information. Therefore, a process is needed to transform it into structured data to be processed further. The information Extraction (IE) process helps to extract relationships, entities, semantic roles, and events from unstructured text data by converting them into structured output. One of IE's tasks is Semantic Role Labeling (SRL), which has a crucial function in identifying semantic roles in a sentence so that it can enrich the understanding of the text. However, much of SRL development focuses on high-resource data, especially in English. The limited development of SRL in specific low-resource languages or domains is a complex challenge. This research aims to conduct a systematic study on the development of SRL for low-resource data, both in low-resource language or domainspecific contexts. The review process was carried out systematically using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and 54 quality papers were obtained from the filtering process (from 2018 to 2023). We review several essential points, including (1) datasets that are often used for SRL tasks and their labeling strategies for low-resource data, (2) methods that have currently been developed for SRL tasks and learning scenarios when dealing with low-resource data, (4) evaluation metrics, (5) application of SRL tasks. This review is complemented by a discussion of issues and potential solutions for developing SRL on low-resource data to help researchers develop SRL more effectively in dealing with the challenges faced with low-resource data.

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