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Register a new userA collaborative approach for research paper recommender system
نظام اقتراح للأوراق البحثية ذو نهج تعاوني
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Damascus University مقالة
Publication date
2019
fields
Informatics Engineering
and research's language is
العربية
Created by
Aya Tello
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إن توافر المعلومات بغزارة عبر الشابكة يجعل البحث عن المعلومات مهمة شاقة. يجد الباحثون صعوبة في الوصول وتتبع الأوراق البحثية الأكثر أهمية بالنسبة لهم. تقدم هذه الورقة مقاربة لنظام اقتراحات للأوراق البحثية في اتجاه تعاوني. من خلال الاستفادة من مزايا نهج التصفية التعاونية، نستخدم البيانات السياقية المتاحة للجميع لاستنتاج الارتباطات الخفية الموجودة بين أوراق البحث من أجل تخصيص الاقتراحات. حداثة النهج المقترح هو أنه يقدم توصيات مخصصة بغض النظر عن مجال البحث وبغض النظر عن خبرة المستخدم.
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Can implicit feedback substitute for explicit ratings in recommender systems?
If so, we could avoid the difficulties associated with gathering explicit ratings from users.
How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations?
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In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence
, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of Who expressed what opinions towards what'' in one sentence. In this work, motivated b
y its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.
هذه المقالة تحوي ترجمة وتلخيص وتوضيح للمذكور في الورقة البحثية المذكور اسمها أعلاه والموجودة في https://annals-csis.org/Volume_8/pliks/221.pdf , والتي تقوم باستخراج المعلومات الدلالية المهمة الموجودة في الوثائق والملفات والأوراق البحثية .
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