Patent
Parsing hierarchical lists and outlines
Ming Ye,Paul A. Viola +1 more
TLDR
This paper used the Collins model for parsing non-textual information into hierarchical content, and assigned labels to lines that indicate how the lines relate to one another in a hierarchical content representation.Abstract:
A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.read more
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Patent
A machine learning system for extracting structured records from web pages and other text sources
Jonathan Baxter,Kirstie Seymore +1 more
TL;DR: In this article, a method for extracting a structured record (190) from a document (100) is described where the structured record includes information related to a predetermined subject matter (120), with this information being organized into categories within structured record.
Patent
Parsing of text using linguistic and non-linguistic list properties
TL;DR: In this article, a system and method for extracting information from text which can be performed without prior knowledge as to whether the text includes a list is described, where parser rules are applied to a sentence spanning lines of text to identify a set of candidate list items in the sentence.
Patent
Generation of documents from images
TL;DR: In this paper, a system for generating soft copy (digital) versions of hard copy documents using images of the hard-copy documents is presented. But the system is limited to the generation of soft copy documents.
Patent
Modifying captured stroke information into an actionable form
TL;DR: In this paper, a computer-implemented technique is described that receives captured stroke information when a user enters a handwritten note using an input capture device, and analyzes the captured strokes to produce output analysis information.
Patent
System and method of digital note taking
Nicolas Rucine,Nathalie Delbecque,Robin Mélinand,Arnoud Boekhoorn,Cédric Coulon,François Bourlieux,Thomas Penin,Aristote Laval,Olivier Patry,Thibault Lelore,Lionel Blestel,Maixent Nziengui Moubamba +11 more
TL;DR: In this article, a system, method and computer program product for use in digital note taking with handwriting input to a computing device are provided, where a user provides input by applying pressure to or gesturing above the input surface using a finger or an instrument.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Journal ArticleDOI
A guided tour to approximate string matching
TL;DR: This work surveys the current techniques to cope with the problem of string matching that allows errors, and focuses on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms.
Proceedings ArticleDOI
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
TL;DR: Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger.
Proceedings Article
Maximum Entropy Markov Models for Information Extraction and Segmentation
TL;DR: A new Markovian sequence model is presented that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences.