Pattern-based approaches to semantic relation extraction

Auger, Alain; Barrière, Caroline

doi:10.1075/term.14.1.02aug

Cited by 69 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to facilitate the formulation of definitions, knowledge-rich contexts (defining and explanatory) are often automatically extracted from available corpora. Commonly, pattern-based methods are used for the task, when pattern templates are constructed in order to identify definitions or explanatory sentences from corpora (Auger & Barrière, 2008;Walter & Pinkal, 2006;Orna-Montesinos, 2011;Bielinskienė et al, 2015). To achieve the most accurate results, the pattern identification templates have to be tested and modified grammatically and lexically.…”

Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Rackevičienė

Mockienė

Utka

et al. 2021

StALan

View full text Add to dashboard Cite

The aim of the paper is to present a methodological framework for the development of an English-Lithuanian bilingual termbase in the cybersecurity domain, which can be applied as a model for other language pairs and other specialised domains. It is argued that the presented methodological approach can ensure creation of high-quality bilingual termbases even with limited available resources. The paper touches upon the methods and problems of dataset (corpora) compilation, terminology annotation, automatic bilingual term extraction (BiTE) and alignment, knowledge-rich context extraction, and linguistic linked open data (LLOD) technologies. The paper presents theoretical considerations as well as the arguments on the effectiveness of the described methods. The theoretical analysis and a pilot study allow arguing that: 1) a combination of parallel and comparable corpora enable to considerably expand the amount and variety of data sources that can be used for terminology extraction; this methodology is especially important for less-resourced languages which often lack parallel data; 2) deep learning systems trained by using manually annotated data (gold standard corpora) allow effective automatization of extraction of terminological data and metadata, which enables to regularly update termbases with minimised manual input; 3) LLOD technologies enable to integrate the terminological data into the global linguistic data ecosystem and make it reusable, searchable and discoverable across the Web.

show abstract

Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Rackevičienė

Mockienė

Utka

et al. 2021

StALan

View full text Add to dashboard Cite

show abstract

“…Самый ранний и простой из них -это подход, основанный на лексико-синтаксических шаблонах. Данный подход, подробно описанный в работе [1], заключается в выявление реальных шаблонов, которые прямо выражают те или иные семантические отношения в тексте и поиске экземпляров таких отношений при помощи этих шаблонов. Сами шаблоны могут представлять собой как просто строки, так и комбинации из частеречных тегов и лексических единиц, что характерно при работе с корпусами.…”

Section: обзор методовunclassified

Extracting Semantic Relations from the Texts of Scientifc Articles

Tikhobaeva

Bruches

Batura

2023

jour

View full text Add to dashboard Cite

Nowadays, the number of scientifc publications existing in the form of electronic text is constantly growing. As a result, the tasks related to the text processing of scientifc articles become especially actual. This paper is dedicated to the task of extracting semantic relations between entities from the texts of scientifc articles in Russian, where we consider scientifc terms as entities. Relation extraction can be useful in some specialized areas, such as searching and question-answering systems, as well as in the compilation of ontologies. In our work, we have created a corpus of scientifc texts consisting of 136 abstracts of scientifc articles in Russian, in which 353 relations of the following types were highlighted: USAGE, ISA, TOOL, SYNONYMS, PART_OF, CAUSE. This corpus was used to train the machine learning models. In addition, we have implemented the automatic semantic relation extraction algorithm and tested it on the already existing corpus RuSERRC. The neural network model BERT was used to implement the algorithm. We’ve done a number of experiments using vectors derived from diﬀerent language models, as well as two neural network architectures. The developed tool and the annotated corpus are publicly available and can be useful for other researchers.

show abstract

“…Typical efforts in Hypernym Discovery can be roughly classified into two main types: rule-based methods and detection-based methods. Rulebased methods (Auger and Barrière, 2008;Kliegr et al, 2008;Seitner et al, 2016;Snow et al, 2004; rely on predefined linguistic rules or patterns to extract hyponym-hypernym relations. These rule-based methods capture specific syntactic or semantic structures that indicate a hierarchical association between terms, however, rule-based methods lack sufficient capability to discover the hyponym-hypernym relation embedded in the semantic content of sentences.…”

Section: Introductionmentioning

confidence: 99%

KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction

Ma,

Wang,

Bao

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Modeling hypernym-hyponym ("is-a") relations is important for many natural language processing (NLP) tasks, such as classification, natural language inference and relation extraction. Existing work on is-a relation extraction is mostly in the English language environment. Due to the flexibility of language expression and the lack of high-quality Chinese annotation datasets, it is still a challenge to accurately identify such relations from Chinese unstructured texts. To tackle this problem, we propose a Knowledge Enhanced Prompt Learning (KEPL) method for Chinese hypernymhyponym relation extraction. Our model uses the Hearst-like patterns as the prior knowledge. By exploiting a Dynamic Adaptor to select the matching pattern for the text into the prompt, our method simultaneously embedding patterns and text. Additionally, we construct a Chinese hypernym-hyponym relation extraction dataset, which contains three typical scenarios, as Baidu Encyclopedia, news and We-media. The experimental results on the dataset demonstrate the efficiency and effectiveness of our proposed model.

show abstract

Pattern-based approaches to semantic relation extraction

Cited by 69 publications

References 15 publications

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Extracting Semantic Relations from the Texts of Scientifc Articles

KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction

Contact Info

Product

Resources

About