Information Extraction Based on Named Entity for Tourism Corpus
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Malware Classification with BERT
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family. -
Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-Agnostic and Domain-Specific Word Embeddings
AAAI 2019 Fall Symposium Series: AI for Social Good 1 Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings Ganesh Nalluru, Rahul Pandey, Hemant Purohit Volgenau School of Engineering, George Mason University Fairfax, VA, 22030 fgn, rpandey4, [email protected] Abstract to provide relevant intelligence inputs to the decision-makers (Hughes and Palen 2012). However, due to the burstiness of The use of social media as a means of communication has the incoming stream of social media data during the time of significantly increased over recent years. There is a plethora of information flow over the different topics of discussion, emergencies, it is really hard to filter relevant information which is widespread across different domains. The ease of given the limited number of emergency service personnel information sharing has increased noisy data being induced (Castillo 2016). Therefore, there is a need to automatically along with the relevant data stream. Finding such relevant filter out relevant posts from the pile of noisy data coming data is important, especially when we are dealing with a time- from the unconventional information channel of social media critical domain like disasters. It is also more important to filter in a real-time setting. the relevant data in a real-time setting to timely process and Our contribution: We provide a generalizable classification leverage the information for decision support. framework to classify relevant social media posts for emer- However, the short text and sometimes ungrammatical nature gency services. We introduce the framework in the Method of social media data challenge the extraction of contextual section, including the description of domain-agnostic and information cues, which could help differentiate relevant vs. -
Injection of Automatically Selected Dbpedia Subjects in Electronic
Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon To cite this version: Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hos- pitalization Prediction. SAC 2020 - 35th ACM/SIGAPP Symposium On Applied Computing, Mar 2020, Brno, Czech Republic. 10.1145/3341105.3373932. hal-02389918 HAL Id: hal-02389918 https://hal.archives-ouvertes.fr/hal-02389918 Submitted on 16 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti Catherine Faron-Zucker Fabien Gandon Université Côte d’Azur, Inria, CNRS, Université Côte d’Azur, Inria, CNRS, Inria, Université Côte d’Azur, CNRS, I3S, Sophia-Antipolis, France I3S, Sophia-Antipolis, France -
Bros:Apre-Trained Language Model for Understanding Textsin Document
Under review as a conference paper at ICLR 2021 BROS: A PRE-TRAINED LANGUAGE MODEL FOR UNDERSTANDING TEXTS IN DOCUMENT Anonymous authors Paper under double-blind review ABSTRACT Understanding document from their visual snapshots is an emerging and chal- lenging problem that requires both advanced computer vision and NLP methods. Although the recent advance in OCR enables the accurate extraction of text seg- ments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces a pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from pre- vious pre-training methods on 1D text, BROS is pre-trained on large-scale semi- structured documents with a novel area-masking strategy while efficiently includ- ing the spatial layout information of input documents. Also, to generate structured outputs in various document understanding tasks, BROS utilizes a powerful graph- based decoder that can capture the relation between text segments. BROS achieves state-of-the-art results on four benchmark tasks: FUNSD, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available. 1 INTRODUCTION Document intelligence (DI)1, which understands industrial documents from their visual appearance, is a critical application of AI in business. One of the important challenges of DI is a key information extraction task (KIE) (Huang et al., 2019; Jaume et al., 2019; Park et al., 2019) that extracts struc- tured information from documents such as financial reports, invoices, business emails, insurance quotes, and many others. -
A Second Look at Word Embeddings W4705: Natural Language Processing
A second look at word embeddings W4705: Natural Language Processing Fei-Tzin Lee October 23, 2019 Fei-Tzin Lee Word embeddings October 23, 2019 1 / 39 Overview Last time... • Distributional representations (SVD) • word2vec • Analogy performance Fei-Tzin Lee Word embeddings October 23, 2019 2 / 39 Overview This time • Homework-related topics • Non-homework topics Fei-Tzin Lee Word embeddings October 23, 2019 3 / 39 Overview Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 4 / 39 GloVe Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 5 / 39 GloVe A recap of GloVe Our motivation: • SVD places too much emphasis on unimportant matrix entries • word2vec never gets to look at global co-occurrence statistics Can we create a new model that balances the strengths of both of these to better express linear structure? Fei-Tzin Lee Word embeddings October 23, 2019 6 / 39 GloVe Setting We'll use more or less the same setting as in previous models: • A corpus D • A word vocabulary V from which both target and context words are drawn • A co-occurrence matrix Mij , from which we can calculate conditional probabilities Pij = Mij =Mi Fei-Tzin Lee Word embeddings October 23, 2019 7 / 39 GloVe The GloVe objective, in overview Idea: we want to capture not individual probabilities of co-occurrence, but ratios of co-occurrence probability between pairs (wi ; wk ) and (wj ; wk ). -
NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and Dlpy Doug Cairns and Xiangxiang Meng, SAS Institute Inc
Paper SAS4429-2020 NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and DLPy Doug Cairns and Xiangxiang Meng, SAS Institute Inc. ABSTRACT A revolution is taking place in natural language processing (NLP) as a result of two ideas. The first idea is that pretraining a deep neural network as a language model is a good starting point for a range of NLP tasks. These networks can be augmented (layers can be added or dropped) and then fine-tuned with transfer learning for specific NLP tasks. The second idea involves a paradigm shift away from traditional recurrent neural networks (RNNs) and toward deep neural networks based on Transformer building blocks. One architecture that embodies these ideas is Bidirectional Encoder Representations from Transformers (BERT). BERT and its variants have been at or near the top of the leaderboard for many traditional NLP tasks, such as the general language understanding evaluation (GLUE) benchmarks. This paper provides an overview of BERT and shows how you can create your own BERT model by using SAS® Deep Learning and the SAS DLPy Python package. It illustrates the effectiveness of BERT by performing sentiment analysis on unstructured product reviews submitted to Amazon. INTRODUCTION Providing a computer-based analog for the conceptual and syntactic processing that occurs in the human brain for spoken or written communication has proven extremely challenging. As a simple example, consider the abstract for this (or any) technical paper. If well written, it should be a concise summary of what you will learn from reading the paper. As a reader, you expect to see some or all of the following: • Technical context and/or problem • Key contribution(s) • Salient result(s) If you were tasked to create a computer-based tool for summarizing papers, how would you translate your expectations as a reader into an implementable algorithm? This is the type of problem that the field of natural language processing (NLP) addresses. -
Cw2vec: Learning Chinese Word Embeddings with Stroke N-Gram Information
The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information Shaosheng Cao,1,2 Wei Lu,2 Jun Zhou,1 Xiaolong Li1 1 AI Department, Ant Financial Services Group 2 Singapore University of Technology and Design {shaosheng.css, jun.zhoujun, xl.li}@antfin.com [email protected] Abstract We propose cw2vec, a novel method for learning Chinese word embeddings. It is based on our observation that ex- ploiting stroke-level information is crucial for improving the learning of Chinese word embeddings. Specifically, we de- sign a minimalist approach to exploit such features, by us- ing stroke n-grams, which capture semantic and morpholog- ical level information of Chinese words. Through qualita- tive analysis, we demonstrate that our model is able to ex- Figure 1: Radical v.s. components v.s. stroke n-gram tract semantic information that cannot be captured by exist- ing methods. Empirical results on the word similarity, word analogy, text classification and named entity recognition tasks show that the proposed approach consistently outperforms Bojanowski et al. 2016; Cao and Lu 2017). While these ap- state-of-the-art approaches such as word-based word2vec and proaches were shown effective, they largely focused on Eu- GloVe, character-based CWE, component-based JWE and ropean languages such as English, Spanish and German that pixel-based GWE. employ the Latin script in their writing system. Therefore the methods developed are not directly applicable to lan- guages such as Chinese that employ a completely different 1. Introduction writing system. Word representation learning has recently received a sig- In Chinese, each word typically consists of less charac- nificant amount of attention in the field of natural lan- ters than English1, where each character conveys fruitful se- guage processing (NLP). -
Effects of Pre-Trained Word Embeddings on Text-Based Deception Detection
2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress Effects of Pre-trained Word Embeddings on Text-based Deception Detection David Nam, Jerin Yasmin, Farhana Zulkernine School of Computing Queen’s University Kingston, Canada Email: {david.nam, jerin.yasmin, farhana.zulkernine}@queensu.ca Abstract—With e-commerce transforming the way in which be wary of any deception that the reviews might present. individuals and businesses conduct trades, online reviews have Deception can be defined as a dishonest or illegal method become a great source of information among consumers. With in which misleading information is intentionally conveyed 93% of shoppers relying on online reviews to make their purchasing decisions, the credibility of reviews should be to others for a specific gain [2]. strongly considered. While detecting deceptive text has proven Companies or certain individuals may attempt to deceive to be a challenge for humans to detect, it has been shown consumers for various reasons to influence their decisions that machines can be better at distinguishing between truthful about purchasing a product or a service. While the motives and deceptive online information by applying pattern analysis for deception may be hard to determine, it is clear why on a large amount of data. In this work, we look at the use of several popular pre-trained word embeddings (Word2Vec, businesses may have a vested interest in producing deceptive GloVe, fastText) with deep neural network models (CNN, reviews about their products. -
Unified Language Model Pre-Training for Natural
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1]. -
Question Answering by Bert
Running head: QUESTION AND ANSWERING USING BERT 1 Question and Answering Using BERT Suman Karanjit, Computer SCience Major Minnesota State University Moorhead Link to GitHub https://github.com/skaranjit/BertQA QUESTION AND ANSWERING USING BERT 2 Table of Contents ABSTRACT .................................................................................................................................................... 3 INTRODUCTION .......................................................................................................................................... 4 SQUAD ............................................................................................................................................................ 5 BERT EXPLAINED ...................................................................................................................................... 5 WHAT IS BERT? .......................................................................................................................................... 5 ARCHITECTURE ............................................................................................................................................ 5 INPUT PROCESSING ...................................................................................................................................... 6 GETTING ANSWER ........................................................................................................................................ 8 SETTING UP THE ENVIRONMENT. .................................................................................................... -
Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep Learning Sentiment Analysis Model During and After Quarantine
Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourchène Ouerhani ( [email protected] ) University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Ahmed Maalel University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Henda Ben Ghézala University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Soulaymen Chouri Vialytics Lautenschlagerstr Research Article Keywords: Chatbot, COVID-19, Natural Language Processing, Deep learning, Mental Health, Ubiquity Posted Date: June 25th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-33343/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Noname manuscript No. (will be inserted by the editor) Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourch`ene Ouerhani · Ahmed Maalel · Henda Ben Gh´ezela · Soulaymen Chouri Received: date / Accepted: date Abstract The huge number of deaths caused by the posed method is a ubiquitous healthcare service that is novel pandemic COVID-19, which can affect anyone presented by its four interdependent modules: Informa- of any sex, age and socio-demographic status in the tion Understanding Module (IUM) in which the NLP is world, presents a serious threat for humanity and so- done, Data Collector Module (DCM) that collect user’s ciety. At this point, there are two types of citizens, non-confidential information to be used later by the Ac- those oblivious of this contagious disaster’s danger that tion Generator Module (AGM) that generates the chat- could be one of the causes of its spread, and those who bots answers which are managed through its three sub- show erratic or even turbulent behavior since fear and modules. -
A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
information Article A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection † Hanqing Zhou *, Amal Zouaq and Diana Inkpen School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada; [email protected] (A.Z.); [email protected] (D.I.) * Correspondence: [email protected]; Tel.: +1-613-562-5800 † This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322. Received: 6 November 2018; Accepted: 20 December 2018; Published: 25 December 2018 Abstract: This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning 1. Introduction The Semantic Web is defined by Berners-Lee et al.