Profile Based Algorithm to Topic Spotting in Reuter21578

This paper proposes an alternative version of SVM using string vectors as its input data. A string vector is defined as a finite ordered set of words. Almost all of machine learning algorithms including traditional versions of SVM use numerical vectors as their input data. In order to apply such machine learning algorithms to classification or clustering, any type of raw data should be encoded into numerical vectors. In text categorization or text clustering, representing documents into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. Although traditional versions of SVM are tolerable to huge dimensionality, they are not robust to sparse distribution in their training and classification. Therefore, in order to avoid this problem, this research proposes another version of SVM, which uses string vectors given as an alternative type of structured data to numerical vectors. For applying the proposed version of SVM to text categorization, documents are encoded into string vectors by defining conditions of words as their features and positioning words corresponding to the conditions into each string vector. A similarity matrix, word by word, which defines semantic similarities of words, should be built from a given training corpus, before using the proposed version of SVM for text categorization. Each semantic similarity given as an element of the matrix is computed based on collocations of two words within their same documents over a given corpus. In this paper, inner product of two string vectors as the proposed kernel function indicates an average semantic similarity over their elements. In order to validate the proposed version of SVM, it will be compared with a traditional version of SVM using linear kernel functions in text categorization on two test beds.

In this research, we propose NTC (Neural Text Categorizer) as the approach to text categorization. Traditional approaches to text categorization require encoding documents into numerical vectors which leads to the two main problems: huge dimensionality and sparse distribution in each numerical vector. In this research, documents are encoded into string vectors instead of numerical vectors, and a new neural network called NTC which receive a string vector as its input vector is used for text categorization. The goal of this research is to avoid the two main problems by encoding documents into alternative structured data to numerical vectors. We will validate the performance of NTC by comparing it with other machine learning algorithms on the standard test bed, Reuter 21578.

This research proposes the application of NTC (neural text categorizer) for categorizing news articles. Even if the research on text categorization has been progressed very much, documents should be still encoded into numerical vectors. Encoding so causes the two main problems: huge dimensionality and sparse distribution. The idea of this research as the solution to the problems is to encode documents into string vectors and apply the NTC as a string vector based approach to text categorization. The idea will be described in detail and validated.

Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anonymous: Search logs can reveal sensitive details about a user, critical articles or messages about a company or government might have severe or fatal consequences for a critic, and negative feedback in customer surveys might negatively impact business relations if they are identified. Simply removing personally identifying information from a document is, however, insufficient to protect the writer's identity: Given some reference texts of suspect authors, so-called authorship attribution methods can reidentfy the author from the text itself. One of the most prominent models to represent documents in many common text mining and information retrieval tasks is the vector space model where each document is represented as a vector, typically containing its term frequencies or related quantities. We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors. We evaluate our method on an exemplary text classification task and demonstrate that it only has a low impact on its accuracy. In contrast, we show that our method strongly affects authorship attribution techniques to the level that they become infeasible with a much stronger decline in accuracy. Other than previous authorship obfuscation methods, our approach is the first that fulfills differential privacy and hence comes with a provable plausible deniability guarantee.

This research proposes a new strategy where documents are encoded into string vectors for text categorization and modified versions of SVM to be adaptable to string vectors. Traditionally, when the supervised machine learning algorithms are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and apply the SVM to string vectors for text categorization.

As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a classifier measures the ability to correctly classify unseen data, interpretability is the ability of the classifier to be understood by humans and provide reasons why each data instance is assigned to a label. This paper proposes an interpretable classification method by exploiting the Dirichlet process mixture model of von Mises---Fisher distributions for directional data. By using the labeled information of the training data explicitly and determining automatically the number of topics for each class, the learned topics are coherent, relevant and discriminative. They help interpret as well as distinguish classes. Our experimental results showed the advantages of our approach in terms of separability, interpretability and effectiveness in classification task of datasets with high dimension and complex distribution. Our method is highly competitive with state-of-the-art approaches.

This research proposes an alternative approach to machine learning based ones for categorizing news articles given as in plain texts. In order to use one of machine learning based approaches for the task, documents should be encoded into numerical vectors; it causes two problems: huge dimensionality and sparse distribution. The proposed approach is intended to address the two problems. In other words, the two problems are avoided by encoding a document or documents into a table, instead of numerical vectors. Therefore, the goal of the research is to improve the performance of text categorization by solving the two problems.

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

Stemming is a fundamental step in processing textual data preceding the tasks of text mining, Information Retrieval (IR), and natural language processing (NLP). The common goal of stemming is to standardize words by reducing a word to its base (root or stem), thus can be also considered a feature reduction technique. This paper aims at presenting a new dictionary free, content-based Arabic stemmer and adopts it as a feature reduction (selection) mechanism to study its contribution in improving Arabic text categorization. We employed three stemming mechanisms (root-based, light, and our stemming technique and assessed their performance in text classification exercises for an Arabic corpus to compare and contrast the text mining effectiveness of these Arabic stemming algorithms. The experiments were conducted on a corpus consisting of 2,966 Arabic documents that fall into three categories: cultural, social, and general. The experiment results showed that our stemmer significantly improved text classification accuracy.

This research proposes NTSO (neural text self organizer) as the approach to text clustering and sets inverted index as the basis for execution of the NTSO. For using one of traditional approaches, documents should be encoded into numerical vectors and encoding so causes the two main problems: the huge dimensionality and the sparse distribution. This research proposes that documents should be encoded into string vectors as the alternative structured forms to numerical vectors and NTSO should be used as the approach to text clustering. By solving the two main problems, the proposed approach is expected to improve the performance of text clustering. By comparing the proposed approach with other approaches, we will validate the text clustering performance of the proposed approach as the results of solving the problems.

Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods such as stemming, stopword filtering and word weighting for Turkish text classification on several different Turkish datasets. We report the results of extensive experiments.

Text representation models are the fundamental basis for information retrieval and text mining tasks. Although different text models have been proposed, they typically target specific task aspects in isolation, such as time efficiency, accuracy, or applicability for different scenarios. Here we present Bag of Textual Graphs (BoTG), a general text representation model that addresses these three requirements at the same time. The proposed textual representation is based on a graph‐based scheme that encodes term proximity and term ordering, and represents text documents into an efficient vector space that addresses all these aspects as well as provides discriminative textual patterns. Extensive experiments are conducted in two experimental scenarios—classification and retrieval—considering multiple well‐known text collections. We also compare our model against several methods from the literature. Experimental results demonstrate that our model is generic enough to handle different tasks and collections. It is also more efficient than the widely used state‐of‐the‐art methods in textual classification and retrieval tasks, with a competitive effectiveness, sometimes with gains by large margins.

Stemming is a technique used to reduce inflected and derived words to their basic forms (stem or root). It is a very important step of pre-processing in text mining, and generally used in many areas of research such as: Natural language Processing NLP, Text Categorization TC, Text Summarizing TS, Information Retrieval IR, and other tasks in text mining. Stemming is frequently useful in text categorization to reduce the size of terms vocabulary, and in information retrieval to improve the search effectiveness and then gives us relevant results. In this paper, we propose a new multilingual stemmer based on the extraction of word root and in which we use the technique of n-grams. We validated our stemmer on three languages which are: Arabic, French and English.

Sentence similarity methods are used to assess the degree of likelihood between phrases. Many natural language applications such as text summarization, information retrieval, text categorization, and machine translation employ measures of sentence similarity. The existing approaches for this problem represent sentences as vectors of bag of words or the syntactic information of the words in the phrase. The likelihood between phrases is calculated by composing the similarity between the words in the sentences. Such schemes do not address two important concerns in the area, however: the semantic problem and the word order. This paper proposes a new sentence similarity measure that attempts to address such problems by taking into account the lexical, syntactic, and semantic analysis of sentences. The new similarity measure proposed outperforms the state of the art systems in around 6%, when tested using a standard and publically available dataset.

Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. Distributed architectures for asynchronous training that have been proposed either focus on scaling vocabulary sizes and dimensionality or suffer from expensive synchronization latencies. In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings. Our training procedure does not involve any parameter synchronization except a final sub-model merge phase that typically executes in a few minutes. Our distributed training scales seamlessly to large corpus sizes and we get comparable and sometimes even up to 45% performance improvement in a variety of NLP benchmarks using models trained by our distributed procedure which requires $1/10$ of the time taken by the baseline approach. Finally we also show that we are robust to missing words in sub-models and are able to effectively reconstruct word representations.