The Growing N-Gram Algorithm: a Novel Approach to String Clustering
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Probabilistic Topic Modelling with Semantic Graph
Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Specifically, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models. -
Intelligent Chat Bot
INTELLIGENT CHAT BOT A. Mohamed Rasvi, V.V. Sabareesh, V. Suthajebakumari Computer Science and Engineering, Kamaraj College of Engineering and Technology, India ABSTRACT This paper discusses the workflow of intelligent chat bot powered by various artificial intelligence algorithms. The replies for messages in chats are trained against set of predefined questions and chat messages. These trained data sets are stored in database. Relying on one machine-learning algorithm showed inaccurate performance, so this bot is powered by four different machine-learning algorithms to make a decision. The inference engine pre-processes the received message then matches it against the trained datasets based on the AI algorithms. The AIML provides similar way of replying to a message in online chat bots using simple XML based mechanism but the method of employing AI provides accurate replies than the widely used AIML in the Internet. This Intelligent chat bot can be used to provide assistance for individual like answering simple queries to booking a ticket for a trip and also when trained properly this can be used as a replacement for a teacher which teaches a subject or even to teach programming. Keywords : AIML, Artificial Intelligence, Chat bot, Machine-learning, String Matching. I. INTRODUCTION Social networks are attracting masses and gaining huge momentum. They allow instant messaging and sharing features. Guides and technical help desks provide on demand tech support through chat services or voice call. Queries are taken to technical support team from customer to clear their doubts. But this process needs a dedicated support team to answer user‟s query which is a lot of man power. -
An Ensemble Regression Approach for Ocr Error Correction
AN ENSEMBLE REGRESSION APPROACH FOR OCR ERROR CORRECTION by Jie Mei Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie University Halifax, Nova Scotia March 2017 © Copyright by Jie Mei, 2017 Table of Contents List of Tables ................................... iv List of Figures .................................. v Abstract ...................................... vi List of Symbols Used .............................. vii Acknowledgements ............................... viii Chapter 1 Introduction .......................... 1 1.1 Problem Statement............................ 1 1.2 Proposed Model .............................. 2 1.3 Contributions ............................... 2 1.4 Outline ................................... 3 Chapter 2 Background ........................... 5 2.1 OCR Procedure .............................. 5 2.2 OCR-Error Characteristics ........................ 6 2.3 Modern Post-Processing Models ..................... 7 Chapter 3 Compositional Correction Frameworks .......... 9 3.1 Noisy Channel ............................... 11 3.1.1 Error Correction Models ..................... 12 3.1.2 Correction Inferences ....................... 13 3.2 Confidence Analysis ............................ 16 3.2.1 Error Correction Models ..................... 16 3.2.2 Correction Inferences ....................... 17 3.3 Framework Comparison ......................... 18 ii Chapter 4 Proposed Model ........................ 21 4.1 Error Detection .............................. 22 -
A Hierarchical Clustering Approach for Dbpedia Based Contextual Information of Tweets
Journal of Computer Science Original Research Paper A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets 1Venkatesha Maravanthe, 2Prasanth Ganesh Rao, 3Anita Kanavalli, 2Deepa Shenoy Punjalkatte and 4Venugopal Kuppanna Rajuk 1Department of Computer Science and Engineering, VTU Research Resource Centre, Belagavi, India 2Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bengaluru, India 3Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India 4Department of Computer Science and Engineering, Bangalore University, Bengaluru, India Article history Abstract: The past decade has seen a tremendous increase in the adoption of Received: 21-01-2020 Social Web leading to the generation of enormous amount of user data every Revised: 12-03-2020 day. The constant stream of tweets with an innate complex sentimental and Accepted: 21-03-2020 contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical Corresponding Authors: Venkatesha Maravanthe use-cases. This paper proposes a scalable context modeling framework for a Department of Computer set of tweets for finding two forms of metadata termed as primary and Science and Engineering, VTU extended contexts. Further, our work presents a hierarchical clustering Research Resource Centre, approach to find hidden patterns by using generated primary and extended Belagavi, India contexts. Ontologies from DBpedia are used for generating primary contexts Email: [email protected] and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. -
Query-Based Multi-Document Summarization by Clustering of Documents
Query-based Multi-Document Summarization by Clustering of Documents Naveen Gopal K R Prema Nedungadi Dept. of Computer Science and Engineering Amrita CREATE Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri, Kollam -690525 Amrita School of Engineering [email protected] Amritapuri, Kollam -690525 [email protected] ABSTRACT based Hierarchical Agglomerative clustering, k-means, Clus- Information Retrieval (IR) systems such as search engines tering Gain retrieve a large set of documents, images and videos in re- sponse to a user query. Computational methods such as Au- 1. INTRODUCTION tomatic Text Summarization (ATS) reduce this information Automatic Text Summarization [12] reduces the volume load enabling users to find information quickly without read- of information by creating a summary from one or more ing the original text. The challenges to ATS include both the text documents. The focus of the summary may be ei- time complexity and the accuracy of summarization. Our ther generic, which captures the important semantic con- proposed Information Retrieval system consists of three dif- cepts of the documents, or query-based, which captures the ferent phases: Retrieval phase, Clustering phase and Sum- sub-concepts of the user query and provides personalized marization phase. In the Clustering phase, we extend the and relevant abstracts based on the matching between input Potential-based Hierarchical Agglomerative (PHA) cluster- query and document collection. Currently, summarization ing method to a hybrid PHA-ClusteringGain-K-Means clus- has gained research interest due to the enormous information tering approach. Our studies using the DUC 2002 dataset load generated particularly on the web including large text, show an increase in both the efficiency and accuracy of clus- audio, and video files etc. -
Practice with Python
CSI4108-01 ARTIFICIAL INTELLIGENCE 1 Word Embedding / Text Processing Practice with Python 2018. 5. 11. Lee, Gyeongbok Practice with Python 2 Contents • Word Embedding – Libraries: gensim, fastText – Embedding alignment (with two languages) • Text/Language Processing – POS Tagging with NLTK/koNLPy – Text similarity (jellyfish) Practice with Python 3 Gensim • Open-source vector space modeling and topic modeling toolkit implemented in Python – designed to handle large text collections, using data streaming and efficient incremental algorithms – Usually used to make word vector from corpus • Tutorial is available here: – https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials – https://rare-technologies.com/word2vec-tutorial/ • Install – pip install gensim Practice with Python 4 Gensim for Word Embedding • Logging • Input Data: list of word’s list – Example: I have a car , I like the cat → – For list of the sentences, you can make this by: Practice with Python 5 Gensim for Word Embedding • If your data is already preprocessed… – One sentence per line, separated by whitespace → LineSentence (just load the file) – Try with this: • http://an.yonsei.ac.kr/corpus/example_corpus.txt From https://radimrehurek.com/gensim/models/word2vec.html Practice with Python 6 Gensim for Word Embedding • If the input is in multiple files or file size is large: – Use custom iterator and yield From https://rare-technologies.com/word2vec-tutorial/ Practice with Python 7 Gensim for Word Embedding • gensim.models.Word2Vec Parameters – min_count: -
NLP - Assignment 2
NLP - Assignment 2 Week 2 December 27th, 2016 1. A 5-gram model is a order Markov Model: (a) Six (b) Five (c) Four (d) Constant Ans : c) Four 2. For the following corpus C1 of 3 sentences, what is the total count of unique bi- grams for which the likelihood will be estimated? Assume we do not perform any pre-processing, and we are using the corpus as given. (i) ice cream tastes better than any other food (ii) ice cream is generally served after the meal (iii) many of us have happy childhood memories linked to ice cream (a) 22 (b) 27 (c) 30 (d) 34 Ans : b) 27 3. Arrange the words \curry, oil and tea" in descending order, based on the frequency of their occurrence in the Google Books n-grams. The Google Books n-gram viewer is available at https://books.google.com/ngrams: (a) tea, oil, curry (c) curry, tea, oil (b) curry, oil, tea (d) oil, tea, curry Ans: d) oil, tea, curry 4. Given a corpus C2, The Maximum Likelihood Estimation (MLE) for the bigram \ice cream" is 0.4 and the count of occurrence of the word \ice" is 310. The likelihood of \ice cream" after applying add-one smoothing is 0:025, for the same corpus C2. What is the vocabulary size of C2: 1 (a) 4390 (b) 4690 (c) 5270 (d) 5550 Ans: b)4690 The Questions from 5 to 10 require you to analyse the data given in the corpus C3, using a programming language of your choice. -
3 Dictionaries and Tolerant Retrieval
Online edition (c)2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 49 Dictionaries and tolerant 3 retrieval In Chapters 1 and 2 we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1 we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2 we study WILDCARD QUERY the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks doc- uments containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for in- stance, the query automat* would seek documents containing any of the terms automatic, automation and automated. We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vo- cabulary terms that are phonetically close to the query term(s). -
Wordnet-Based Metrics Do Not Seem to Help Document Clustering
Wordnet-based metrics do not seem to help document clustering Alexandre Passos1 and Jacques Wainer1 Instituto de Computação (IC) Universidade Estadual de Campinas (UNICAMP) Campinas, SP, Brazil Abstract. In most document clustering systems documents are repre- sented as normalized bags of words and clustering is done maximizing cosine similarity between documents in the same cluster. While this representation was found to be very effective at many different types of clustering, it has some intuitive drawbacks. One such drawback is that documents containing words with similar meanings might be considered very different if they use different words to say the same thing. This happens because in a traditional bag of words, all words are assumed to be orthogonal. In this paper we examine many possible ways of using WordNet to mitigate this problem, and find that WordNet does not help clustering if used only as a means of finding word similarity. 1 Introduction Document clustering is now an established technique, being used to improve the performance of information retrieval systems [11], as an aide to machine translation [12], and as a building block of narrative event chain learning [1]. Toolkits such as Weka [3] and NLTK [10] make it easy for anyone to experiment with document clustering and incorporate it into an application. Nonetheless, the most commonly accepted model—a bag of words representation [17], bisecting k-means algorithm [19] maximizing cosine similarity [20], preprocessing the corpus with a stemmer [14], tf-idf weighting [17], and a possible list of stop words [2]—is complex, unintuitive and has some obvious negative consequences. -
Use of Word Embedding to Generate Similar Words and Misspellings for Training Purpose in Chatbot Development
USE OF WORD EMBEDDING TO GENERATE SIMILAR WORDS AND MISSPELLINGS FOR TRAINING PURPOSE IN CHATBOT DEVELOPMENT by SANJAY THAPA THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at The University of Texas at Arlington December, 2019 Arlington, Texas Supervising Committee: Deokgun Park, Supervising Professor Manfred Huber Vassilis Athitsos Copyright © by Sanjay Thapa 2019 ACKNOWLEDGEMENTS I would like to thank Dr. Deokgun Park for allowing me to work and conduct the research in the Human Data Interaction (HDI) Lab in the College of Engineering at the University of Texas at Arlington. Dr. Park's guidance on the procedure to solve problems using different approaches has helped me to grow personally and intellectually. I am also very thankful to Dr. Manfred Huber and Dr. Vassilis Athitsos for their constant guidance and support in my research. I would like to thank all the members of the HDI Lab for their generosity and company during my time in the lab. I also would like to thank Peace Ossom Williamson, the director of Research Data Services at the library of the University of Texas at Arlington (UTA) for giving me the opportunity to work as a Graduate Research Assistant (GRA) in the dataCAVE. i DEDICATION I would like to dedicate my thesis especially to my mom and dad who have always been very supportive of me with my educational and personal endeavors. Furthermore, my sister and my brother played an indispensable role to provide emotional and other supports during my graduate school and research. ii LIST OF ILLUSTRATIONS Fig: 2.3: Rasa Architecture ……………………………………………………………….7 Fig 2.4: Chatbot Conversation without misspelling and with misspelling error. -
Feature Combination for Measuring Sentence Similarity
FEATURE COMBINATION FOR MEASURING SENTENCE SIMILARITY Ehsan Shareghi Nojehdeh A thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Master of Computer Science Concordia University Montreal,´ Quebec,´ Canada April 2013 c Ehsan Shareghi Nojehdeh, 2013 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Ehsan Shareghi Nojehdeh Entitled: Feature Combination for Measuring Sentence Similarity and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining commitee: Chair Dr. Peter C. Rigby Examiner Dr. Leila Kosseim Examiner Dr. Adam Krzyzak Supervisor Dr. Sabine Bergler Approved Chair of Department or Graduate Program Director 20 Dr. Robin A. L. Drew, Dean Faculty of Engineering and Computer Science Abstract Feature Combination for Measuring Sentence Similarity Ehsan Shareghi Nojehdeh Sentence similarity is one of the core elements of Natural Language Processing (NLP) tasks such as Recognizing Textual Entailment, and Paraphrase Recognition. Over the years, different systems have been proposed to measure similarity between fragments of texts. In this research, we propose a new two phase supervised learning method which uses a combination of lexical features to train a model for predicting similarity between sentences. Each of these features, covers an aspect of the text on implicit or explicit level. The two phase method uses all combinations of the features in the feature space and trains separate models based on each combination. -
The Research of Weighted Community Partition Based on Simhash
Available online at www.sciencedirect.com Procedia Computer Science 17 ( 2013 ) 797 – 802 Information Technology and Quantitative Management (ITQM2013) The Research of Weighted Community Partition based on SimHash Li Yanga,b,c, **, Sha Yingc, Shan Jixic, Xu Kaic aInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190 bGraduate School of Chinese Academy of Sciences, Beijing, 100049 cNational Engineering Laboratory for Information Security Technologies, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093 Abstract The methods of community partition in nowadays mainly focus on using the topological links, and consider little of the content-based information between users. In the paper we analyze the content similarity between users by the SimHash method, and compute the content-based weight of edges so as to attain a more reasonable community partition. The dataset adopt the real data from twitter for testing, which verify the effectiveness of the proposed method. © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the organizers of the 2013 International Conference on Information Technology and Quantitative Management Keywords: Social Network, Community Partition, Content-based Weight, SimHash, Information Entropy; 1. Introduction The important characteristic of social network is the community structure, but the current methods mainly focus on the topological links, the research on the content is very less. The reason is that we can t effectively utilize content-based information from the abundant short text. So in the paper we hope to make full use of content-based information between users based on the topological links, so as to find the more reasonable communities.