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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Paper Categorization Using Naive Bayes

Cui, Man 29 April 2013 (has links)
Literature survey is a time-consuming process as researchers spend a lot of time in searching the papers of interest. While search engines can be useful in finding papers that contain a certain set of keywords, one still has to go through these papers in order to decide whether they are of interest. On the other hand, one can quickly decide which papers are of interest if each one of them is labelled with a category. The process of labelling each paper with a category is termed paper categorization, an instance of a more general problem called text classification. In this thesis, we presented a text classifier called Iris that makes use of the popular Naive Bayes algorithm. With Iris, we were able to (1) evaluate Naive Bayes using a number of popular datasets, (2) propose a GUI for assisting users with document categorization and searching, and (3) demonstrate how the GUI can be utilized for paper categorization and searching. / Graduate / 0984

The Use of Distributional Semantics in Text Classification Models : Comparative performance analysis of popular word embeddings

Norlund, Tobias January 2016 (has links)
In the field of Natural Language Processing, supervised machine learning is commonly used to solve classification tasks such as sentiment analysis and text categorization. The classical way of representing the text has been to use the well known Bag-Of-Words representation. However lately low-dimensional dense word vectors have come to dominate the input to state-of-the-art models. While few studies have made a fair comparison of the models' sensibility to the text representation, this thesis tries to fill that gap. We especially seek insight in the impact various unsupervised pre-trained vectors have on the performance. In addition, we take a closer look at the Random Indexing representation and try to optimize it jointly with the classification task. The results show that while low-dimensional pre-trained representations often have computational benefits and have also reported state-of-the-art performance, they do not necessarily outperform the classical representations in all cases.

アンカーテキストとハイパーリンクに基づくWeb 文書の階層的分類

鈴木, 祐介, Suzuki, Yusuke, 松原, 茂樹, Matsubara, Shigeki, 吉川, 正俊, Yoshikswa, Masatoshi 06 1900 (has links)
No description available.

Cross-lingual genre classification

Petrenz, Philipp January 2014 (has links)
Automated classification of texts into genres can benefit NLP applications, in that the structure, location and even interpretation of information within a text are dictated by its genre. Cross-lingual methods promise such benefits to languages which lack genre-annotated training data. While there has been work on genre classification for over two decades, none has considered cross-lingual methods before the start of this project. My research aims to fill this gap. It follows previous approaches to monolingual genre classification that exploit simple, low-level text features, many of which can be extracted in different languages and have similar functions. This contrasts with work on cross-lingual topic or sentiment classification of texts that typically use word frequencies as features. These have been shown to have limited use when it comes to genres. Many such methods also assume cross-lingual resources, such as machine translation, which limits the range of their application. A selection of these approaches are used as baselines in my experiments. I report the results of two semi-supervised methods for exploiting genre-labelled source language texts and unlabelled target language texts. The first is a relatively simple algorithm that bridges the language gap by exploiting cross-lingual features and then iteratively re-trains a classification model on previously predicted target texts. My results show that this approach works well where only few cross-lingual resources are available and texts are to be classified into broad genre categories. It is also shown that further improvements can be achieved through multi-lingual training or cross-lingual feature selection if genre-annotated texts are available in several source languages. The second is a variant of the label propagation algorithm. This graph-based classifier learns genre-specific feature set weights from both source and target language texts and uses them to adjust the propagation channels for each text. This allows further feature sets to be added as additional resources, such as Part of Speech taggers, become available. While the method performs well even with basic text features, it is shown to benefit from additional feature sets. Results also indicate that it handles fine-grained genre classes better than the iterative re-labelling method.

A Large Collection Learning Optimizer Framework

Chakravarty, Saurabh 30 June 2017 (has links)
Content is generated on the web at an increasing rate. The type of content varies from text on a traditional webpage to text on social media portals (e.g., social network sites and microblogs). One such example of social media is the microblogging site Twitter. Twitter is known for its high level of activity during live events, natural disasters, and events of global importance. Challenges with the data in the Twitter universe include the limit of 140 characters on the text length. Because of this limitation, the vocabulary in the Twitter universe includes short abbreviations of sentences, emojis, hashtags, and other non-standard usage. Consequently, traditional text classification techniques are not very effective on tweets. Fortunately, sophisticated text processing techniques like cleaning, lemmatizing, and removal of stop words and special characters will give us clean text which can be further processed to derive richer word semantic and syntactic relationships using state of the art feature selection techniques like Word2Vec. Machine learning techniques, using word features that capture semantic and context relationships, can be of benefit regarding classification accuracy. Improving text classification results on Twitter data would pave the way to categorize tweets relative to human defined real world events. This would allow diverse stakeholder communities to interactively collect, organize, browse, visualize, analyze, summarize, and explore content and sources related to crises, disasters, human rights, inequality, population growth, resiliency, shootings, sustainability, violence, etc. Having the events classified into different categories would help us study causality and correlations among real world events. To check the efficacy of our classifier, we would compare our experimental results with an Association Rules (AR) classifier. This classifier composes its rules around the most discriminating words in the training data. The hierarchy of rules, along with an ability to tune to a support threshold, makes it an effective classifier for scenarios where short text is involved. Traditionally, developing classification systems for these purposes requires a great degree of human intervention. Constantly monitoring new events, and curating training and validation sets, is tedious and time intensive. Significant human capital is required for such annotation endeavors. Also, involved efforts are required to tune the classifier for best performance. Developing and tuning classifiers manually using human intervention would not be a viable option if we are to monitor events and trends in real-time. We want to build a framework that would require very little human intervention to build and choose the best among the available performing classification techniques in our system. Another challenge with classification systems is related to their performance with unseen data. For the classification of tweets, we are continually faced with a situation where a given event contains a certain keyword that is closely related to it. If a classifier, built for a particular event, due to overfitting to what is a biased sample with limited generality, is faced with new tweets with different keywords, accuracy may be reduced. We propose building a system that will use very little training data in the initial iteration and will be augmented with automatically labelled training data from a collection that stores all the incoming tweets. A system that is trained on incoming tweets that are labelled using sophisticated techniques based on rich word vector representation would perform better than a system that is trained on only the initial set of tweets. We also propose to use sophisticated deep learning techniques like Convolutional Neural Networks (CNN) that can capture the combination of the words using an n-gram feature representation. Such sophisticated feature representation could account for the instances when the words occur together. We divide our case studies into two phases: preliminary and final case studies. The preliminary case studies focus on selecting the best feature representation and classification methodology out of the AR and the Word2Vec based Logistic Regression classification techniques. The final case studies focus on developing the augmented semi-supervised training methodology and the framework to develop a large collection learning optimizer to generate a highly performant classifier. For our preliminary case studies, we are able to achieve an F1 score of 0.96 that is based on Word2Vec and Logistic Regression. The AR classifier achieved an F1 score of 0.90 on the same data. For our final case studies, we are able to show improvements of F1 score from 0.58 to 0.94 in certain cases based on our augmented training methodology. Overall, we see improvement in using the augmented training methodology on all datasets. / Master of Science

Knowledge-enhanced text classification : descriptive modelling and new approaches

Martinez-Alvarez, Miguel January 2014 (has links)
The knowledge available to be exploited by text classification and information retrieval systems has significantly changed, both in nature and quantity, in the last years. Nowadays, there are several sources of information that can potentially improve the classification process, and systems should be able to adapt to incorporate multiple sources of available data in different formats. This fact is specially important in environments where the required information changes rapidly, and its utility may be contingent on timely implementation. For these reasons, the importance of adaptability and flexibility in information systems is rapidly growing. Current systems are usually developed for specific scenarios. As a result, significant engineering effort is needed to adapt them when new knowledge appears or there are changes in the information needs. This research investigates the usage of knowledge within text classification from two different perspectives. On one hand, the application of descriptive approaches for the seamless modelling of text classification, focusing on knowledge integration and complex data representation. The main goal is to achieve a scalable and efficient approach for rapid prototyping for Text Classification that can incorporate different sources and types of knowledge, and to minimise the gap between the mathematical definition and the modelling of a solution. On the other hand, the improvement of different steps of the classification process where knowledge exploitation has traditionally not been applied. In particular, this thesis introduces two classification sub-tasks, namely Semi-Automatic Text Classification (SATC) and Document Performance Prediction (DPP), and several methods to address them. SATC focuses on selecting the documents that are more likely to be wrongly assigned by the system to be manually classified, while automatically labelling the rest. Document performance prediction estimates the classification quality that will be achieved for a document, given a classifier. In addition, we also propose a family of evaluation metrics to measure degrees of misclassification, and an adaptive variation of k-NN.

Using sentence-level classification to predict sentiment at the document-level

Hutton, Amanda Rachel 21 August 2012 (has links)
This report explores various aspects of sentiment mining. The two research goals for the report were: (1) to determine useful methods in increasing recall of negative sentences and (2) to determine the best method for applying sentence level classification to the document level. The methods in this report were applied to the Movie Reviews corpus at both the document and sentence level. The basic approach was to first identify polar and neutral sentences within the text and then classify the polar sentences as either positive or negative. The Maximum Entropy classifier was used as the baseline system in which the application of further methods was explored. Part-of-speech tagging was used for its effectiveness to determine if its inclusion increased recall of negative sentences. It was also used to aid in the handling of negations within sentences at the sentence level. Smoothing was investigated and various metrics to describe the sentiment composition were explored to address goal (2). Negative recall was shown to increase with the adjustment of the classification threshold and was also seen to increase through the methods used to address goal (2). Overall, classifying at the sentence level using bigrams and a cutoff value of one was observed to result in the highest evaluation scores. / text

Automatic Document Classification Applied to Swedish News

Blein, Florent January 2005 (has links)
The first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN). The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories. This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories. The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment. The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.

AI Approaches for Classification and Attribute Extraction in Text

Magnusson, Ludvig, Rovala, Johan January 2017 (has links)
As the amount of data online grows, the urge to use this data for different applications grows as well. Machine learning can be used with the intent to reconstruct and validate the data you are interested in. Although the problem is very domain specific, this report will attempt to shed some light on what we call strategies for classification, which in broad terms mean, a set of steps in a process where the end goal is to have classified some part of the original data. As a result, we hope to introduce clarity into the classification process in detail as well as from a broader perspective. The report will investigate two classification objectives, one of which is dependent on many variables found in the input data and one that is more literal and only dependent on one or two variables. Specifically, the data we will classify are sales-objects. Each sales-object has a text describing the object and a related image. We will attempt to place these sales-objects into the correct product category. We will also try to derive the year of creation and it’s dimensions such as height and width. Different approaches are presented in the aforementioned strategies in order to classify such attributes. The results showed that for broader attributes such as a product category, supervised learning is indeed an appropriate approach, while the same can not be said for narrower attributes, which instead had to rely on entity recognition. Experiments on image analytics in conjunction with supervised learning proved image analytics to be a good addition when requiring a higher precision score.

Time to Open the Black Box : Explaining the Predictions of Text Classification

Löfström, Helena January 2018 (has links)
The purpose of this thesis has been to evaluate if a new instance based explanation method, called Automatic Instance Text Classification Explanator (AITCE), could provide researchers with insights about the predictions of automatic text classification and decision support about documents requiring human classification. Making it possible for researchers, that normally use manual classification, to cut time and money in their research, with the maintained quality. In the study, AITCE was implemented and applied to the predictions of a black box classifier. The evaluation was performed at two levels: at instance level, where a group of 3 senior researchers, that use human classification in their research, evaluated the results from AITCE from an expert view; and at model level, where a group of 24 non experts evaluated the characteristics of the classes. The evaluations indicate that AITCE produces insights about which words that most strongly affect the prediction. The research also suggests that the quality of an automatic text classification may increase through an interaction between the user and the classifier in situations with unsure predictions.

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