Text categorization

Text categorization aims to automatically assign given text passages or documents to predetermined categories or subjects. Despite the wide array of techniques employed in classifying English text, there remains a dearth of research on Vietnamese text classification. This paper introduces a novel approach utilizing a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with a deep network structure for Vietnamese text classification.

10p viambani 18-06-2024 3 1   Download

Part 2 book "Handbook of psychology - Vol 4: Experimental psychology" includes content: Motor control, conditioning and learning, animal memory and cognition, sensory and working memory, semantic memory and priming, episodic and autobiographical memory, procedural memory and skill acquisition, language comprehension and production, reading, text comprehension and discourse processing, concepts and categorization, reasoning and problem solving, psychological experimentation addressing practical concerns.

396p muasambanhan08 23-02-2024 1 1   Download

In recent times, we have witnessed dramatic progresses and emergence of advanced deep neural architectures in natural language processing (NLP) domain. The advanced sequence-to-sequence (seq2seq)/transformer based architectures have demonstrated remarkable improvements in multiple NLP’s tasks, including text categorization.

10p viannee 02-08-2023 6 5   Download

This paper presents a reliable method for categorizing emergency of messages in Tweeter. We rely on representation of text features by image patterns instead of using original features extracted from text message.

11p viharry 15-12-2022 10 3   Download

This article sets out the theoretical background of semantic mapping as a way in order to help them focus their attention on background knowledge in the reading process so that they can work out the meaning of a text.

27p vimarillynhewson 17-05-2022 19 3   Download

Student's Book Pack 1: Skillful Reading & Writing - Part 2 provide students with lessons about distinguish facts and opinions to categorize information; identify the tone a text to understand the writer's attitude or purpose; practice scanning texts to finds key details quickly; organize your notes using charts when reading;...

55p chenlinong_0310 23-02-2022 25 7   Download

To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder.

18p spiritedaway36 28-11-2021 11 0   Download

Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R&D directions.

38p spiritedaway36 25-11-2021 12 0   Download

This study examines the semantic contribution of English suffixes and prefixes in the translation process by categorizing and analyzing various English affixes in the source texts and proposing their Vietnamese equivalent for the target texts. The results reveal that implementing an affixal analysis in the translation process (1) enhances the translation quality, (2) accelerates the translation process, and (3) conveys patterns of meaning associated with affixal stress and intonation.

12p trinhthamhodang1219 06-05-2021 20 2   Download

A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents.

12p viwyoming2711 16-12-2020 13 1   Download

In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations.

12p vikentucky2711 24-11-2020 9 3   Download

Shrimp farming is a key sector in economic development in Mekong Delta provinces. Unfortunately, there are many problems in shrimp farming, especially shrimp diseases which cause a considerable loss. Shrimp diseases are expressed through symptoms and manifestations of shrimp.

8p quenchua9 20-11-2020 15 1   Download

This paper proposed a hybrid GA rule based categorization method, named genetic algorithm rule based categorization (GARC), to enhance the accuracy of categorization rules and to produce accurate classifier for text mining.

14p tohitohi 22-05-2020 11 1   Download

The development of the Internet has increased the need for daily online information storage. Finding the correct information that we are interested in takes a lot of time, so the use of techniques for organizing and processing text data are needed. These techniques are called text classification or text categorization. There are many methods of text classification, but for this paper we study and apply the Support Vector Machine (SVM) method and compare its effect with the Naïve Bayes probability method.

17p caothientrangnguyen 01-04-2020 62 2   Download

Automatic detection of general relations between short texts is a complex task that cannot be carried out only relying on language models and bag-of-words. Therefore, learning methods to exploit syntax and semantics are required. In this paper, we present a new kernel for the representation of shallow semantic information along with a comprehensive study on kernel methods for the exploitation of syntactic/semantic structures for short text pair categorization.

9p bunthai_1 06-05-2013 48 4   Download

We introduce two novel methods of text categorization in which documents are split into fragments. We conducted experiments on English, French and Czech. In all cases, the problems referred to a binary document classification. We find that both methods increase the accuracy of text categorization. For the Na¨ve Bayes classifier this increase is ı significant.

4p bunbo_1 17-04-2013 27 1   Download

This paper describes several ongoing projects that are united by the theme of changes in lexical use over time. We show that paying attention to a document’s temporal context can lead to improvements in information retrieval and text categorization. We also explore a potential application in document clustering that is based upon different types of lexical changes.

6p bunbo_1 17-04-2013 38 1   Download

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as “thumbs up” or “thumbs down”. To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. ...

8p bunbo_1 17-04-2013 64 1   Download

A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, often prohibitive, number of labeled training documents for accurate learning. Generally, acquiring class labels for training data is costly, while gathering a large quantity of unlabeled data is cheap. We here propose a new automatic text categorization method for learning from only unlabeled data using a bootstrapping framework and a feature projection technique.

8p bunbo_1 17-04-2013 58 1   Download

We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars” is intuitively closer to “four stars” than to “one star”. We first evaluate human performance at the task.

10p bunbo_1 17-04-2013 53 2   Download