Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.

Shallow semantics for relation extraction

Abstract: This paper presents a new method for extracting meaningful relations from unstructured natural language sources. The method is based on information made available by shallow semantic parsers. Semantic information was used (1) to enhance a dependency tree kernel; and (2) to build semantic dependency structures used for enhanced relation extraction for several semantic classifiers. In our experiments the quality of the extracted relations surpassed the results of kernel-based models employing only semantic class information.

phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Abstract: Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.

Statistics and neural networks: advances at the interface

Abstract: Flexible discriminant and mixture models Neural networks for unsupervised learning based on information theory Radial basis function networks and statistics Robust prediction in many-parameter models Density networks Latent variable models and data visualisation Analysis of latent structure models with multidimensional latent variables Artificial neural networks and multivariate statistics

FLSA: Extending Latent Semantic Analysis with Features for Dialogue Act Classification

Abstract: We discuss Feature Latent Semantic Analysis (FLSA), an extension to Latent Semantic Analysis (LSA). LSA is a statistical method that is ordinarily trained on words only; FLSA adds to LSA the richness of the many other linguistic features that a corpus may be labeled with. We applied FLSA to dialogue act classification with excellent results. We report results on three corpora: CallHome Spanish, MapTask, and our own corpus of tutoring dialogues.

Similarity measures based on latent dirichlet allocation

Abstract: We present in this paper the results of our investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty aspects, while the Latent Semantic Analysis measures are used for comparison purposes. We explore two types of measures based on Latent Dirichlet Allocation: measures based on distances between probability distribution that can be applied directly to larger texts such as sentences and a word-to-word similarity measure that is then expanded to work at sentence-level. We present results using paraphrase identification data in the Microsoft Research Paraphrase corpus.