Do you want to publish a course? Click here

Developing a multilayer semantic annotation scheme based on ISO standards for the visualization of a newswire corpus

تطوير مخطط شروح دلالات متعدد الطبقات يعتمد على معايير ISO لتصور كوربوس Newswire

246   0   0   0.0 ( 0 )
 Publication date 2021
and research's language is English
 Created by Shamra Editor




Ask ChatGPT about the research

In this paper, we describe the process of developing a multilayer semantic annotation scheme designed for extracting information from a European Portuguese corpus of news articles, at three levels, temporal, referential and semantic role labelling. The novelty of this scheme is the harmonization of parts 1, 4 and 9 of the ISO 24617 Language resource management - Semantic annotation framework. This annotation framework includes a set of entity structures (participants, events, times) and a set of links (temporal, aspectual, subordination, objectal and semantic roles) with several tags and attribute values that ensure adequate semantic and visual representations of news stories.

References used
https://aclanthology.org/

rate research

Read More

Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in rece nt years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.
This paper presents work carried out to transform glosses of a fable in Italian Sign Language (LIS) into a text which is then read by a TTS synthesizer from an SSML modified version of the same text. Whereas many systems exist that generate sign lang uage from a text, we decided to do the reverse operation and generate text from LIS. For that purpose we used a version of the fable The Tortoise and the Hare, signed and made available on Youtube by ALBA cooperativa sociale, which was annotated manually by second author for her master's thesis. In order to achieve our goal, we converted the multilayer glosses into linear Prolog terms to be fed to the generator. In the paper we focus on the main problems encountered in the transformation of the glosses into a semantically and pragmatically consistent representation. The main problems have been caused by the complexities of a text like a fable which requires coreference mechanisms and speech acts to be implemented in the representation which are often unexpressed and constitute implicit information.
In recent decades, the number of children in nurseries has increased significantly throughout the world, increasing their exposure to communicable diseases, so this analytical survey study aimed to develop standards for control of communicable dis eases in nurseries at Lattakia and to assess the procedures and practices applied to control these diseases, two groups of elements were used: the first group included thirty experts from different fields and specialties as a committee of arbitrators for the initial standards and the second included 20 nurseries selected from 80 nurseries located in Lattakia using cluster random sampling method, two tools were developed: to initial standards and to assess the procedures and practices applied to control of communicable diseases.
In this paper we describe the process of build-ing a corporate corpus that will be used as a ref-erence for modelling and computing threadsfrom conversations generated using commu-nication and collaboration tools. The overallgoal of the reconstructio n of threads is to beable to provide value to the collorator in var-ious use cases, such as higlighting the impor-tant parts of a running discussion, reviewingthe upcoming commitments or deadlines, etc. Since, to our knowledge, there is no avail-able corporate corpus for the French languagewhich could allow us to address this prob-lem of thread constitution, we present here amethod for building such corpora includingdifferent aspects and steps which allowed thecreation of a pipeline to pseudo-anonymisedata. Such a pipeline is a response to theconstraints induced by the General Data Pro-tection Regulation GDPR in Europe and thecompliance to the secrecy of correspondence.
The streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance of these uploaded videos. However, the present analysis functions of YouTube only provide a few performance indicators such as average view duration, browsing history, variance in audience's demographics, etc., and lack of sentiment analysis on the audience's comments. Therefore, the paper proposes multi-dimensional sentiment indicators such as YouTuber preference, Video preferences, and Excitement level to capture comprehensive sentiment on audience comments for videos and YouTubers. To evaluate the performance of different classifiers, we experiment with deep learning-based, machine learning-based, and BERT-based classifiers to automatically detect three sentiment indicators of an audience's comments. Experimental results indicate that the BERT-based classifier is a better classification model than other classifiers according to F1-score, and the sentiment indicator of Excitement level is quite an improvement. Therefore, the multiple sentiment detection tasks on the video streaming service platform can be solved by the proposed multi-dimensional sentiment indicators accompanied with BERT classifier to gain the best result.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا