Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEvaluation of Taxonomy Enrichment on Diachronic WordNet Versions
تقييم التخصيب التصنيفي في إصدارات WordNet DIACHRONIC
310
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تطبق الغالبية العظمى من الأساليب الحالية لتخصيص التصنيفات في تطبق Adgeddings Word لأنها أثبتت تجميع السياقات (بمعنى واسع) المستخرجة من النصوص التي تكفي إرفاق الكلمات الأيتام بالتصنيف.من ناحية أخرى، وبصرف النظر عن كونها الموارد الكبيرة المعجمية واللاللالية، فإن التصنيفات هي هياكل رسم بيانية.يمكن أن يكون الجمع بين تدمير Word مع هيكل الرسم البياني للتصنيف موضع التنبؤ بالتنبؤ بالعلاقات التصنيفية.في هذه الورقة، نقارن العديد من النهج لإرفاق كلمات جديدة بالتصنيف الموجود القائمة على تمثيلات الرسم البياني مع تلك التي تعتمد على ASTTEXT AGEDDINGS.نختبر جميع الأساليب على مجموعات البيانات الروسية والإنجليزية، ولكن يمكن تطبيقها أيضا على الكلمات واللغات الأخرى.
The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.
References used
https://aclanthology.org/
rate research
Read More
The use of euphemisms is a known driver of language change. It has been proposed that women use euphemisms more than men. Although there have been several studies investigating gender differences in language, the claim about euphemism usage has not b
een tested comprehensively through time. If women do use euphemisms more, this could mean that women also lead the formation of new euphemisms and language change over time. Using four large diachronic text corpora of English, we evaluate the claim that women use euphemisms more than men through a quantitative analysis. We assembled a list of 106 euphemism-taboo pairs to analyze their relative use through time by each gender in the corpora. Contrary to the existing belief, our results show that women do not use euphemisms with a higher proportion than men. We repeated the analysis using different subsets of the euphemism-taboo pairs list and found that our result was robust. Our study indicates that in a broad range of settings involving both speech and writing, and with varying degrees of formality, women do not use or form euphemisms more than men.
This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. Th
e increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.
Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the
detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.
Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through inter
lingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.
In this paper, we present a major update to the first Hungarian named entity dataset, the Szeged NER corpus. We used zero-shot cross-lingual transfer to initialize the enrichment of entity types annotated in the corpus using three neural NER models:
two of them based on the English OntoNotes corpus and one based on the Czech Named Entity Corpus finetuned from multilingual neural language models. The output of the models was automatically merged with the original NER annotation, and automatically and manually corrected and further enriched with additional annotation, like qualifiers for various entity types. We present the evaluation of the zero-shot performance of the two OntoNotes-based models and a transformer-based new NER model trained on the training part of the final corpus. We release the corpus and the trained model.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
