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The document provides an overview of text categorization using machine learning. It discusses feature extraction from text like bag-of-words representations and term weighting. It also covers different machine learning algorithms commonly used for text categorization like Naive Bayes, k-Nearest Neighbors, Boosting, and Support Vector Machines. The document concludes by mentioning hierarchical learning approaches and areas for future work in natural language processing with machine learning.
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https://github.com/hezam2022/text-categorization
activelearning.ppt
Active learning aims to improve machine learning models using less training data by strategically selecting the most informative data points to be labeled. It is important because manually labeling data can be time-consuming and expensive. The core problem is how to actively select the most informative training points to query labels for. Different active learning methods, such as using neural networks, Bayesian models, and support vector machines, aim to query points that the current model is most uncertain about. Combining active learning with expectation maximization using a large pool of unlabeled data can improve text classification when only a small amount of labeled training data is available.
Search Engines
This document discusses classification and clustering techniques used in search engines. It covers classification tasks like spam detection, sentiment analysis, and ad classification. Naive Bayes and support vector machines are described as common classification approaches. Features, feature selection, and evaluation metrics for classifiers are also summarized.
T EXT M INING AND C LASSIFICATION OF P RODUCT R EVIEWS U SING S TRUCTURED S U...
Text mining and Text classification are the two pro
minent and challenging tasks in the field of
Machine learning. Text mining refers to the process
of deriving high quality and relevant
information from text, while Text classification de
als with the categorization of text documents
into different classes. The real challenge in these
areas is to address the problems like handling
large text corpora, similarity of words in text doc
uments, and association of text documents with
a subset of class categories. The feature extractio
n and classification of such text documents
require an efficient machine learning algorithm whi
ch performs automatic text classification.
This paper describes the classification of product
review documents as a multi-label
classification scenario and addresses the problem u
sing Structured Support Vector Machine.
The work also explains the flexibility and performan
ce of the proposed approach for e
fficient text classification.
pmuthoju_presentation.ppt
The document discusses using support vector machines (SVM) for automatic document categorization. It proposes using an SVM trained on a collection of documents that have been manually categorized into fields and groups. Documents are represented as sparse vectors of words and their TF-IDF weights. An SVM is trained for each category on a subset of documents. The trained SVMs are then used to categorize new documents by predicting the likelihood they belong to each category. The method achieved good recall and precision on test documents from several sample categories. Improvements and future work expanding the approach are also discussed.
DETERMINING CUSTOMER SATISFACTION IN-ECOMMERCE
Derric A. Alkis C
Abstract:
Delivering the customer to a high degree of confidence and the seller for more information about the products and the desire of customers through the use of modern technology and Machine Learning through comments left on the product to see and evaluate the comments added later and thus evaluate the product, whether good or bad.
IRJET- Multi Label Document Classification Approach using Machine Learning Te...
This document discusses multi-label document classification approaches using machine learning techniques. It first provides background on multi-label classification and surveys several existing methods. These include transductive multi-label learning via label set propagation, classifier chains for multi-label classification, and multilabel neural networks. It also discusses random k-label sets and graph-based substructure pattern mining techniques. The document then proposes a new multi-label classification system that uses machine learning algorithms with semi-supervised learning and assigns weights to labels during testing to classify instances. Finally, it concludes that existing methods have issues like high computational complexity and missing data that the proposed approach aims to address.
Review of Various Text Categorization Methods
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
C017321319
The document reviews various text categorization methods and proposes a new supervised term weighting method using normalized term frequency and relevant frequency (ntf.rf). It begins by discussing existing text categorization methods and their limitations. Specifically, existing methods often require labeled training data, cleaned datasets, and work best on linearly separable data. The document then proposes the new ntf.rf method to address these limitations by incorporating preprocessing and leveraging both normalized term frequency and relevant frequency to assign term weights. Finally, the document outlines how ntf.rf could improve text categorization by providing a more effective term weighting approach.
Paper id 25201435
The document describes an algorithmic approach to keyword extraction and text document classification. It discusses using naive Bayes and support vector machine (SVM) classifiers with keyword and key phrases extracted via porter stemming as training data. The algorithm performs preprocessing like stop word removal and stemming. Features are selected based on term frequency-inverse document frequency (TF-IDF). Documents are represented as term-document matrices. Naive Bayes and SVM are then applied for classification and compared, with the goal of improving supervised and unsupervised classification accuracy.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
Machine_learning_presentation_on_movie_recomendation_system.pptx
The document describes a movie recommendation system project that uses machine learning. It includes sections on the problem statement, recommendation systems, the project workflow including data collection, processing, text vectorization, building a user interface, accuracy metrics, and results. The goal is to recommend movies to users based on their preferences using collaborative filtering techniques.
Team G
This document describes a web page categorization application that uses support vector machines (SVM) for text categorization. The application extracts text from web pages, parses it to extract noun terms, creates document vectors, and trains an SVM classifier on labeled training data to categorize new pages. The system was implemented with a client-server structure where the client is a web browser that sends page text to the server for processing and categorization. The SVM classifier was trained on over 7,500 labeled pages and achieved an accuracy of over 70% on a test set. The application allows users to easily add pages to categorized favorites lists within their browser.
Learning from similarity and information extraction from structured documents...
https://www.infrrd.ai/blog/learning-from-similarity-and-information-extraction-from-structured-documents
Part 1
This tutorial covers machine learning approaches for learning to rank documents in information retrieval systems. It discusses how early IR methods did not incorporate machine learning. It then covers: (1) ordinal regression approaches that learn multiple thresholds to account for the ordered nature of relevance labels; (2) optimizing pairwise preferences between documents, which decomposes the problem and allows for efficient algorithms; (3) directly optimizing rank-based evaluation measures like MAP and NDCG using structural SVMs, boosting, or smooth approximations to allow for gradient descent optimization of discontinuous objectives. The goal is to outperform traditional IR methods by applying machine learning techniques to learn good ranking functions.
Text Document Classification System
This document discusses a text document classification system using machine learning algorithms. It aims to classify newspaper articles into different sections like business, sports, etc. The system involves preprocessing text data, training classification models using algorithms like KNN, Naive Bayes, SVM and random forest. Hyperparameter tuning is performed to improve model performance using techniques like k-fold cross validation and grid search. The models are evaluated on a BBC news dataset containing over 1400 articles in 5 categories. The goal is to design a multi-label text classification model with optimized hyperparameters.
Mariia Havrylovych "Active learning and weak supervision in NLP projects"
Successful artificial intelligence solutions always require a massive amount of high-quality labeled data. In most cases, we don’t have a large and qualitative labeled set together. Weak supervision and active learning tools may help you optimize the labeling process and address the shortage of data labels.
First, we will review how active learning can significantly reduce the amount of labeled data for training with classic approaches. We will show how active learning methods can be customized for a specific (NLP) task by using text embedding.
With weak supervision, we will see how using simple rules gets a big train dataset automatically and high model performance without manual labeling at all.
In the end, we will combine active learning and weak supervision by taking advantage of both techniques and achieving the best metrics.
Similar to [ppt] (20)
A Survey Of Various Machine Learning Techniques For Text Classification
A Survey Of Various Machine Learning Techniques For Text Classification
T EXT M INING AND C LASSIFICATION OF P RODUCT R EVIEWS U SING S TRUCTURED S U...
T EXT M INING AND C LASSIFICATION OF P RODUCT R EVIEWS U SING S TRUCTURED S U...
IRJET- Multi Label Document Classification Approach using Machine Learning Te...
IRJET- Multi Label Document Classification Approach using Machine Learning Te...
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE
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A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE
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Machine_learning_presentation_on_movie_recomendation_system.pptx
Machine_learning_presentation_on_movie_recomendation_system.pptx
Learning from similarity and information extraction from structured documents...
Learning from similarity and information extraction from structured documents...
Mariia Havrylovych "Active learning and weak supervision in NLP projects"
Mariia Havrylovych "Active learning and weak supervision in NLP projects"
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[ppt]
- 1. A Survey on Text Categorization with Machine Learning Chikayama lab. Dai Saito
- 21. Simple example of Boosting + + + + + － － － － － + + + + + － － － － － 1. － － + + + + + － － － 2. + + + + + － － － － － 3.
- 26. TreeBoost root L1 L2 L3 L4 L11 L12 L41 L42 L43 L421 L422
Editor's Notes
- インターネットの普及やコンピュータを用いた文書の電子化が進むにつれて、 メールやニュース、ブログ等、大量の電子化されたデータが入手可能となってきた。 それに従い、時間や人的コストの観点から、 人手を介さずに大量の文書を効率良く分類する必要が高まってきている。
- 例えばテキストを自動的にどのトピックに属するかを調べたり、 Webからの評判を抽出、といった応用が挙げられる。
- そこで、テキストを自動で分類するための手法として最も広く用いられているのが、 単語などのテキスト情報を元にした機械学習の手法である。 機械学習は広く分けて教師あり、教師無し、があるが、 本輪講では教師あり学習について述べる
- ここでテキスト分類における機械学習の主な流れを示す。 まず、自然言語で書かれたテキストを機械が扱えるような形に変換する。 （特徴抽出） そしてその特徴を用いて学習器で学習する。 （学習） 未知のデータが来た場合、訓練した学習器を元にデータを分類する。 （分類） このようにテキスト分類は一般で用いられる機械学習の流れとほぼ同じなため、 機械学習の分野で広く研究されている。 ここでは、このそれぞれの段階について用いられている手法の調査を行う。
- ここではテキストデータからの特徴抽出について説明する。 まず、自然言語で書かれたデータを形態素解析等を用いて何らかの数値データに変換する必要がある。
- この場合、例えば英語で言えばthe, for, 等の非常に頻繁に出てくる単語は「ストップワード」として 取り除かれる必要がある。
- まず最初に思いつく最も単純な方法として、各単語の出現回数を数える方法が考えられる。 文書数×単語数のベクトルを考え、どの文書にどの単語が何回出現するのか、を表す。 この場合、非常に単純にデータを扱うことが出来るが、出現回数のみを見ているのであまり精度が出ない
- ここで考えられるのが tf-idf 法である。 これは、（単語がある文書に出てくる頻度） × （単語が出てくる文書数の逆数）をとったもので、 文書に頻繁に出てきて、また全体ではあまり出てこない単語に高い重みがつくようになっており、 テキスト分類における特徴抽出の方法として広く用いられている。 基本的に文書の特徴は tf-idf か、あるいはこの値を正規化したものを用いることが 事実上標準となっており、新たな研究はあまり行われていない。
- 上のままだと文書を表すベクトルが文書数×辞書の単語数、とかなり大きくなってしまう。 そこで、この次元数を削減するために特徴選択が用いられる。
- ここで用いられているものは、まず一つは出現頻度に特定のスレッショルドを設けることである。 単語が出てくる文書数一定回以上出てない単語は学習に用いない。 これは、非常に少ない文書にしか出てこない単語は分類の役に立たないであろう、という推測に基づいている。