Computational extraction of formulaic sequences from corpora

Wahl, Alexander; Gries, Stefan Τh.

doi:10.1075/ivitra.24.05wah

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…At the same time, string matching and regular expressions can also be used to identify formulaic language ( Durrant & Mathews-Aydnl, 2011 ). Through multiple linear regression analysis, Wahl (2019) found that the number of rules is positively correlated with the types of recognized formulaic language. The more rules are made, the more types of formulaic language are recognized.…”

Section: Related Workmentioning

confidence: 99%

Formulaic language identification model based on GCN fusing associated information

Meng

Zheng

Bao

et al. 2022

PeerJ Computer Science

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Formulaic language is a general term for ready-made structures in a language. It usually has fixed grammatical structure, stable language expression meaning and specific use context. The use of formulaic language can coordinate sentence generation in the process of writing and communication, and can significantly improve the idiomaticity and logic of machine translation, intelligent question answering and so on. New formulaic language is generated almost every day, and how to accurately identify them is a topic worthy of research. To this end, this article proposes a formulaic language identification model based on GCN fusing associated information. The innovation is that each sentence is constructed into a graph in which the nodes are part-of-speech features and semantic features of the words in the sentence and the edges between nodes are constructed according to mutual information and dependency syntactic relation. On this basis, the graph convolutional neural network is adopted to extract the associated information between words to mine deeper grammatical features. Therefore, it can improve the accuracy of formulaic language identification. The experimental results show that the model in this article is superior to the classical formulaic language identification model in terms of accuracy, recall and F1-score. It lays a foundation for the follow-up research of formulaic language identification tasks.

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Section: Related Workmentioning

confidence: 99%

Formulaic language identification model based on GCN fusing associated information

Meng

Zheng

Bao

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…However, not much work was done on the automatic recognition of longer routine expressions. Wahl and Gries [19] is an exception, but they still focus on the phrase level and units shorter than complete sentences. Finding reused text passages and sentences is often important for the analysis of documents.…”

Section: Related Workmentioning

confidence: 99%

Representing Standard Text Formulations as Directed Graphs

Josi¹,

Wartena²,

Heid³

2021

Document Analysis and Recognition – ICDAR 2021 Workshops

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In order to ensure validity in legal texts like contracts and case law, lawyers rely on standardised formulations that are written carefully but also represent a kind of code with a meaning and function known to all legal experts. Using directed (acyclic) graphs to represent standardized text fragments, we are able to capture variations concerning time specifications, slight rephrasings, names, places and also OCR errors. We show how we can find such text fragments by sentence clustering, pattern detection and clustering patterns. To test the proposed methods, we use two corpora of German contracts and court decisions, specially compiled for this purpose. However, the entire process for representing standardised text fragments is language-agnostic. We analyze and compare both corpora and give an quantitative and qualitative analysis of the text fragments found and present a number of examples from both corpora.

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“…With the advent of the technology, the computers are nowadays used to retrieve the linguistics information from textual data which is known as Computational Linguistics (CL) [8]- [9]. CL is classified into many categories but among them context clues, semantic, and syntactic [9]- [11] matching is widely used in the domain of linguistics. CL helps in identifying and matching of related words from input datasets with the data dictionary which is known as domain knowledge [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multidimensional Reference Model for Heterogeneous Textual Datasets using Context, Semantic and Syntactic Clues

Kumar,

Imam,

Mamman

et al. 2023

IJACSA

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With the advent of technology and use of latest devices, they produce voluminous data. Out of it, 80% of the data are unstructured and remaining 20% are structured and semistructured. The produced data are in heterogeneous format and without following any standards. Among heterogeneous (structured, semi-structured and unstructured) data, textual data are nowadays used by industries for prediction and visualization of future challenges. Extracting useful information from it is really challenging for stakeholders due to lexical and semantic matching. Few studies have been solving this issue by using ontologies and semantic tools, but the main limitations of proposed work were the less coverage of multidimensional terms. To solve this problem, this study aims to produce a novel multidimensional reference model using linguistics categories for heterogeneous textual datasets. The categories in such context, semantic and syntactic clues are focused along with their score. The main contribution of MRM is that it checks each tokens with each term based on indexing of linguistic categories such as synonym, antonym, formal, lexical word order and cooccurrence. The experiments show that the percentage of MRM is better than the state-of-the-art single dimension reference model in terms of more coverage, linguistics categories and heterogeneous datasets.

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Computational extraction of formulaic sequences from corpora

Cited by 10 publications

References 21 publications

Formulaic language identification model based on GCN fusing associated information

Formulaic language identification model based on GCN fusing associated information

Representing Standard Text Formulations as Directed Graphs

A Novel Multidimensional Reference Model for Heterogeneous Textual Datasets using Context, Semantic and Syntactic Clues

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