95,206 research outputs found
Text segmentation with character-level text embeddings
Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.Comment: Workshop on Deep Learning for Audio, Speech and Language Processing,
ICML 201
Segmenting broadcast news streams using lexical chains
In this paper we propose a course-grained NLP approach to text segmentation based on the
analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual
units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The absence of large scale datasets with pixel-level supervisions is a
significant obstacle for the training of deep convolutional networks for scene
text segmentation. For this reason, synthetic data generation is normally
employed to enlarge the training dataset. Nonetheless, synthetic data cannot
reproduce the complexity and variability of natural images. In this paper, a
weakly supervised learning approach is used to reduce the shift between
training on real and synthetic data. Pixel-level supervisions for a text
detection dataset (i.e. where only bounding-box annotations are available) are
generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which
provides pixel-level supervisions for the COCO-Text dataset, is created and
released. The generated annotations are used to train a deep convolutional
neural network for semantic segmentation. Experiments show that the proposed
dataset can be used instead of synthetic data, allowing us to use only a
fraction of the training samples and significantly improving the performances
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