다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

/pdf/from-frequency-to-meaning-vector-space-models-of-semantics-12a9pz4gzm.pdf

From frequency to meaning: vector space models of semantics

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

/pdf/semeval-2017-task-4-sentiment-analysis-in-twitter-5294l6x1f7.pdf

SemEval-2017 Task 4: Sentiment Analysis in Twitter

This article suggests that while mental health problems affect 10—20% of children and adolescents worldwide, the mental health needs of children and adolescents are neglected, especially in low-income and middle-income countries. The authors review the evidence and the gaps in the published work in terms of prevalence, risk and protective factors, and interventions to prevent and treat childhood and adolescent mental health problems.

Child and adolescent mental health worldwide: evidence for action

A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surface forms of the lexical nodes are in part or completely different The experiments with such kernels for question classification show an unprecedented results, eg 41% of error reduction of the former state-of-the-art Additionally, semantic role classification confirms the benefit of semantic smoothing for dependency kernels

/pdf/structured-lexical-similarity-via-convolution-kernels-on-2n3ved0uq2.pdf

Structured Lexical Similarity via Convolution Kernels on Dependency Trees

Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In many real scenarios, obtaining high- quality annotated data is expensive and time consuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected. One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi- Supervised Generative Adversarial Networks. In this paper, we propose GAN-BERT that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks.

/pdf/gan-bert-generative-adversarial-learning-for-robust-text-2m9j7vzo07.pdf

GAN-BERT: Generative adversarial learning for robust text classification with a bunch of labeled examples

The Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA) is the biennial initiative aimed at promoting the development of language and speech technologies for the Italian language. EVALITA is promoted by the Italian Association of Computational Linguistics (AILC)1 and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA)2 and the Italian Association for Speech Sciences (AISV)3. EVALITA provides a shared framework where different systems and approaches can be scientifically evaluated and compared with each other with respect to a large variety of tasks, suggested and organized by the Italian research community. The proposed tasks represent scientific challenges where methods, resources, and systems can be tested against shared benchmarks representing linguistic open issues or real world applications, possibly in a multilingual and/or multi-modal perspective. The collected data sets provide big opportunities for scientists to explore old and new problems concerning NLP in Italian as well as to develop solutions and to discuss the NLP-related issues within the community. Some tasks are traditionally present in the evaluation campaign, while others are completely new. This paper introduces the tasks proposed at EVALITA 2020 and provides an overview to the participants and systems whose descriptions and obtained results are reported in these Proceedings4. The EVALITA 2020 edition, held online on December 17th due to the COVID-19 pandemic, counts 14 different tasks. In particular, the selected tasks are grouped in five research areas (tracks) according to their objective and characteristics, namely (i) Affect, Hate, and Stance, (ii) Creativity and Style, (iii) New Challenges in Long-standing Tasks, (iv) Semantics and Multimodality, (v) Time and Diachrony. This edition was highly participated, with 51 groups whose participants have affiliation in 14 countries. Although EVALITA is generally promoted and targeted to the Italian research community, this edition saw an international participation, also thanks to the fact that several Italian researchers working in different countries contributed to the organization of the tasks or participated in them as authors. This overview is organized as follows: in Section 2 a brief description of the tasks belonging to the various areas is reported. Section 3 discusses the participation to the workshop referred to several aspects, from the research area, to the affiliation of authors. Section 4 describes the criteria used to assign the best system across tasks award, made by an ad-hoc committee starting from the suggestions of task organizers and reviewers. Finally, section 5 points out on both the obtained results and on the future of the workshop.

/pdf/evalita-2020-overview-of-the-7th-evaluation-campaign-of-59xy0bw19p.pdf

EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian.

English. The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification , polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets – which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language – the evaluation methodology, and discuss results and participating systems.

/pdf/overview-of-the-evalita-2016-sentiment-polarity-15pbvnqmst.pdf

Overview of the Evalita 2016 SENTIment POLarity Classification Task

This paper describes the KeLP system participating in the SemEval-2016 Community Question Answering (cQA) task. The challenge tasks are modeled as binary classification problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and produce the final ranking. All classifiers and kernels have been implemented within the Kernel-based Learning Platform called KeLP. Our primary submission ranked first in Subtask A, third in Subtask B and second in Subtask C. These ranks are based on MAP, which is the referring challenge system score. Our approach outperforms all the other systems with respect to all the other challenge metrics.

/pdf/kelp-at-semeval-2016-task-3-learning-semantic-relations-23bl05p0xj.pdf

Danilo Croce

Papers

Structured Lexical Similarity via Convolution Kernels on Dependency Trees

GAN-BERT: Generative adversarial learning for robust text classification with a bunch of labeled examples

EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian.

Overview of the Evalita 2016 SENTIment POLarity Classification Task

KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers