Multilingual Sentence Categorization according to Language

In this paper, we describe an approach to sentence categorization which has the originality to be based on natural properties of languages with no training set dependency. The implementation is fast, small, robust and textual errors tolerant. Tested for french, english, spanish and german discrimination, the system gives very interesting results, achieving in one test 99.4% correct assignments on real sentences. The resolution power is based on grammatical words (not the most common words) and alphabet. Having the grammatical words and the alphabet of each language at its disposal, the system computes for each of them its likelihood to be selected. The name of the language having the optimum likelihood will tag the sentence --- but non resolved ambiguities will be maintained. We will discuss the reasons which lead us to use these linguistic facts and present several directions to improve the system's classification performance. Categorization sentences with linguistic properties shows that difficult problems have sometimes simple solutions.Comment: 4 pages --- LaTe

PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks

Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular machine learning tasks. One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. Although the low dimensional representations learned are applicable to many different tasks, they are not particularly tuned for any task. In this paper, we fill this gap by proposing a semi-supervised representation learning method for text data, which we call the \textit{predictive text embedding} (PTE). Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. The labeled information and different levels of word co-occurrence information are first represented as a large-scale heterogeneous text network, which is then embedded into a low dimensional space through a principled and efficient algorithm. This low dimensional embedding not only preserves the semantic closeness of words and documents, but also has a strong predictive power for the particular task. Compared to recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much more efficient, and has fewer parameters to tune.Comment: KDD 201

Text categorization and similarity analysis: similarity measure, architecture and design

This research looks at the most appropriate similarity measure to use for a document classification problem. The goal is to find a method that is accurate in finding both semantically and version related documents. A necessary requirement is that the method is efficient in its speed and disk usage. Simhash is found to be the measure best suited to the application and it can be combined with other software to increase the accuracy. Pingar have provided an API that will extract the entities from a document and create a taxonomy displaying the relationships and this extra information can be used to accurately classify input documents. Two algorithms are designed incorporating the Pingar API and then finally an efficient comparison algorithm is introduced to cut down the comparisons required