This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research co
ntributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicate-object triples to form a knowledge graph with the help of multiclass classification and multi-label classification. We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results. The average F1-score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F1-score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.
We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important contribution sentences i
n a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Pharse 1, our approach yields the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://github.com/Liu-Hy/nlp-contrib-graph.
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union's Horizon 2020 research and innovation program. The collected resources were offered to participants of a hackathon organized a
s part of the EACL Hackashop on News Media Content Analysis and Automated Report Generation in February 2021. The hackathon had six participating teams who addressed different challenges, either from the list of proposed challenges or their own news-industry-related tasks. This paper goes beyond the scope of the hackathon, as it brings together in a coherent and compact form most of the resources developed, collected and released by the EMBEDDIA project. Moreover, it constitutes a handy source for news media industry and researchers in the fields of Natural Language Processing and Social Science.
This research aims to propose laws Actuarial assessment of pension component first
of contributions, secondly from net investment income to the pension fund and to estimate
the reserve mathematical as well, also aims toestimate pension and reserve
athlete in the
Pension Fund Agricultural Engineers Association in Syria at the end of 2009
It has been reached through this research that each agricultural engineer referred to
retire at the end of 2009 deservesa pension every month of 9480 SP, where the bulk of this
salary are contributions from Member's cumulative for thirty years, with a 8596 S.P. The
remaining amount is estimated as 884.SP. It maybe from the net investment income of the
Fund.
The reserves have been estimated Mathematical Total of the contributing Members
in the pension fundof about6795million SP, this reserve should be kept in the pension fund
to meet future salary pensions to retired members.
