Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the of cropped line images, ignoring error caused by detection in real-world applications. Although some approaches aimed at page-level have proposed recent years, they either are limited to simple layouts or require very detailed annotations including expens...

Pancreatic islet [Formula: see text]-cells are electrically excitable cells that secrete insulin in an oscillatory fashion when the blood glucose concentration is at a stimulatory level. Insulin oscillations are the result of cytosolic [Formula: see text] oscillations that accompany bursting electrical activity of [Formula: see text]-cells and are physiologically important. ATP-sensitive [Formu...

We present a named entity recognition (NER) system for tagging fiction: LitNER. Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of handtagged data or gazetteers, instead it bootstraps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf s...

Clustering is an extensively studied data mining problem in the text domains. The difficulty finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In text mining, clustering the sentence is one of the processes and used within general text mining tasks. Several clustering methods and algorithms are used...

This article proposes to auto-encode text at byte-level using convolutional networks with a recursive architecture. The motivation is to explore whether it is possible to have scalable and homogeneous text generation at byte-level in a nonsequential fashion through the simple task of auto-encoding. We show that nonsequential text generation from a fixed-length representation is not only possibl...

Text mining is defined as knowledge discovery in large text collections. It detects interesting patterns such as clusters, associations, deviations, similarities, and differences in sets of texts. Current text mining methods use simplistic representations of text contents, such as keyword vectors, which imply serious limitations on the kind and meaningfulness of possible discoveries. We show ho...

This paper demonstrates a new method for leveraging free-text annotations to infer semantic properties of documents. Free-text annotations are becoming increasingly abundant, due to the recent dramatic growth in semistructured, user-generated online content. An example of such content is product reviews, which are often annotated by their authors with pros/cons keyphrases such as “a real bargai...

The problems of character recognition are today mainly due to imperfect thresholding and segmentation In this paper a new ap proach to text recognition is presented which attempts to avoid these problems by working directly on grey level images and treating an en tire word at the time The features are found from the grey levels of the image and a hidden Markov model is de ned for each character...

High quality linguistic features is the key to the success of speech synthesis. Traditional linguistic feature extraction methods are usually relied on a word-level natural language processing (NLP) parser. Since, a good parser requires a lot of feature engineering to build, it is usually a genral-purpose one and often not specially designed for speech synthesis. To avoid these difficulties, we...

Non-standard language as it appears in user-generated content has recently attracted much attention. This paper proposes that non-standardness comes in two basic varieties, technical and linguistic, and develops a machine-learning method to discriminate between standard and nonstandard texts in these two dimensions. We describe the manual annotation of a dataset of Slovene user-generated conten...