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检索条件"任意字段=Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing"

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Big Data Approach to Developing Adaptable Corpus Tools 4

Big Data Approach to Developing Adaptable Corpus Tools

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4th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Lutskiv, Andriy Popovych, Nataliya Ternopil Ivan Puluj Natl Tech Univ Comp Syst & Networks Dept Ternopol Ukraine Uzhgorod Natl Univ State Univ Dept Multicultural Educ & Translat Uzhgorod Ukraine

Thesis deals with the development of corpus tools which allow building corpus of religious and historical texts. It is foreseen that the corpus has the features of data ingestion, text data preprocessing, statistics calculation, qualitative and quantitative text analysis. All these features are customizable. With Big Data approach is meant that corpus tools are treated as the data platform and the corpus itself is treated as a combination of data lake and data warehouse solutions. There have been suggested the ways for resolving algorithmic, methodological and architectural problems which arise while building corpus tool. The effectiveness of natural language processing and natural language understanding methods, libraries and tools on the example of building historical and religious texts' corpora have been checked. There have been created the workflows which comprise data extraction from sources, data transformation, data enrichment and loading into corpus storage with proper qualitative and quantitative characteristics. Data extraction approaches which are common for ingestion into data lake were used. Transformations and enrichments were realized by means of natural language processing and natural language understanding techniques. Calculation of statistical characteristics was done by means of machine learning techniques. Finding keywords and relations between them became possible thanks to the employment of latent semantic analysis, terms and N-gram frequencies, term frequency-inverse document frequencies. Computation complexity and number of information noise were reduced by singular value decomposition. The influence of singular value decomposition parameters on the text processing accuracy has been analyzed. The results of corpus-based computational experiment for religious text concept analysis have been shown. The architectural approaches to building corpus-based data platform and the usage of software tools, frameworks and specific libraries have been su

关键词： adaptable text corpus Big Data natural language processing natural language understanding statistics machine learning data mining conceptual analysis corpus-based translation studies conceptual seme componential analysis

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graph-based semi-supervised learning for natural language understanding 13

Graph-based semi-supervised learning for natural language un...

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13th workshop on graph-based methods for natural language processing, Textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Qiu, Zimeng Cho, Eunah Ma, Xiaochun Campbell, William M. Electrical and Computer Engineering Department Carnegie Mellon University United States Amazon Alexa AI

ISBN: (纸本)9781950737864

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graphbased semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach's applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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Identifying supporting facts for multi-hop question answering with document graph networks 13

Identifying supporting facts for multi-hop question answerin...

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作者： Thayaparan, Mokanarangan Valentino, Marco Schlegel, Viktor Freitas, Andre Department of Computer Science University of Manchester United Kingdom

ISBN: (纸本)9781950737864

Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infer the correct answer. This paper proposes Document graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text. The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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Predicting the increase in postoperative motor deficits in patients with supratentorial gliomas using machine learning methods 23

Predicting the increase in postoperative motor deficits in p...

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Supplementary 23rd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2021

作者： Kosyrkova, Alexandra Ilyushin, Eugene Saada, Daniel Afandiev, Ramin Baev, Alexander Pogosbekyan, Eudard Okhlopkov, Vladimir Danilov, Gleb Batalov, Artem Pronin, Igor Zakharova, Natalia Ogurtsova, Anna Kravchuck, Alexander Pitskhelauri, David Potapov, Alexander Goryaynov, Sergey "N. N. Burdenko National Medical Research Center of Neurosurgery" of the Ministry of Health of the Russian Federation Lomonosov Moscow State University Russia

Surgery of glial tumors of the brain located in the motor areas vicinity is associated with a high risk of increasing neurological deficits. Motor deficit affects overall survival in this group of patients. Nowadays, no method allows for an objective preoperative data-based prognosis of the risk of neurological impairment in each particular case. Objective: develop a convolution neuronal network that can predict motor worsening in patients with supranational gliomas using the preoperative MRI-data. Materials and methods: the study included 527 patients aged 18 years and older with newly identified supratentorial gliomas. All patients underwent preoperative MRI and tumor removal based on Burdenko National Center of neurosurgery in 2013-2019. Data on motor status dynamics after surgery for these patients were obtained from the electronic medical records using the original semiautomatic algorithm for natural language processing. The T2FLAIR mode is used for training our model. The model demonstrates the following metrics of quality: accuracy 91%, sensitivity 94%, specificity 89%, ROC AUC 91%, and F1 92%. Thus, machine learning methods predict the motor worsening with relatively high accuracy in patients with supratentorial gliomas at the preoperative stage, based on brain MRI data. Copyright © 2021 for this paper by its authors.

关键词： Convolution

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Red dragon AI at Textgraphs 2019 shared task: language model assisted explanation generation 13

Red dragon AI at TextGraphs 2019 shared task: Language model...

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作者： Chia, Yew Ken Witteveen, Sam Andrews, Martin Red Dragon AI Singapore Singapore

ISBN: (纸本)9781950737864

The Textgraphs-13 Shared Task on Explanation Regeneration (Jansen and Ustalov, 2019) asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Our leaderboard submission placed us 3rd in the competition, but we present here three methods of increasing sophistication, each of which scored successively higher on the test set after the competition close. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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Joint semantic and distributional word representations with multi-graph embeddings 13

Joint semantic and distributional word representations with ...

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作者： Moreux, Pierre-Daix Galle, Matthias Ubisoft Entertainment SA France Naver Labs Europe France

ISBN: (纸本)9781950737864

Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases while the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping both distributional and semantic information into a multi-graph and modifying existing node embeddings techniques to compute word representations. Our experiments show improved results compared to vanilla word embeddings, retrofitting and concatenation techniques using the same information, on a variety of data-sets of word similarities. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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Mitigating File-Injection Attacks with natural language processing 6

Mitigating File-Injection Attacks with Natural Language Proc...

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6th ACM International workshop on Security and Privacy Analytics (IWSPA)

作者： Liu, Hao Wang, Boyang Univ Cincinnati Cincinnati OH 45221 USA

ISBN: (纸本)9781450371155

Searchable Encryption can bridge the gap between privacy protection and data utilization. As it leaks access pattern to attain practical search performance, it is vulnerable under advanced attacks. While these advanced attacks show significant privacy leakage, the assumptions of these attacks are often strong and the methods that can be used to mitigate these attacks are limited. In this paper, we investigate one of these advanced attacks, referred to as file-injection attacks, and examine whether this attack is viable in practice. In addition, we also propose a defense method to mitigatefi le-injection attacks. By leveraging natural language processing, we formulate the generation of injectedfi les in the attack as an automated text generation problem with restrictions on word selection, and then we tackle the problem with n-grams and Recursive Neural Networks. We formulate the proposed defense as a semantic analysis problem, in which we extract linguistic features and address the problem using machine learning. Our experiential results on real-world datasets suggest two interesting observations. First, automatically generating injectedfi les in the attack will result low semantics infi les. Second, it is viable to automatically detect injectedfi les based on semantics and mitigatefi le-injection attacks.

关键词： Searchable Encryption File-Injection Attacks natural language processing Machine Learning

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ASU at Textgraphs 2019 shared task: Explanation regeneration using language models and iterative re-ranking 13

ASU at TextGraphs 2019 shared task: Explanation regeneration...

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作者： Banerjee, Pratyay School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781950737864

In this work we describe the system from natural language processing group at Arizona State University for the Textgraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Iterative methods

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Event detection from news in indian languages using similarity based pattern finding approach 12

Event detection from news in indian languages using similari...

引用

Working Notes of FIRE - 12th Forum for Information Retrieval Evaluation, FIRE-WN 2020

作者： Basak, Shubham Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata WB700 108 India

In this work, we propose a rule based method to identify the event type and create a frame for that event. With the help of natural language Toolkit (NLTK) and preloaded SpaCy models, we have tried to define certain methods to identify the event (for task 1) and create the event frame from the given article (for task 2). © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： natural language processing systems

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Neural speech translation using lattice transformations and graph networks 13

Neural speech translation using lattice transformations and ...

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作者： Beck, Daniel Cohn, Trevor Haffari, Gholamreza School of Computing and Information Systems University of Melbourne Australia Faculty of Information Technology Monash University Australia

ISBN: (纸本)9781950737864

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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