In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: We present a concrete design for Solomonoff’s incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.

A: Gigamachine: incremental machine learning on desktop computers
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Q: In this paper we examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English and a Greek corpus used for text segmentation. The aim here is to examine whether the combination of text segmentation and information extraction can be beneficial for the identification of the various topics that appear in a document. NER was performed manually in the English corpus and was compared with the output produced by publicly available annotation tools while, an already existing tool was used for the Greek corpus. Produced annotations from both corpora were manually corrected and enriched to cover four types of named entities. Co-reference resolution i.e., substitution of every reference of the same instance with the same named entity identifier was subsequently performed. The evaluation, using five text segmentation algorithms for the English corpus and four for the Greek corpus leads to the conclusion that, the benefit highly depends on the segment’s topic, the number of named entity instances appearing in it, as well as the segment’s length.

A: Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts
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Q: This paper describes a new method for classifying a dataset that partitions elements into different categories. It has relations with neural networks but works in a different way, requiring only a single pass through the classifier to generate the weight sets. A grid structure is required and a novel idea of converting a row of real values into a 2-D or grid-like structure of value bands. Each cell in the band can then store a cell weight value and also a set of weights that represent its own importance to each of the output categories. For any input that needs to be categorised, all of the output weight lists for each relevant input cell can be retrieved and summed to produce a probability for what the correct output category is. So the relative importance of each input point to the output is distributed to each cell. The construction process itself can simply be the reinforcement of the weight values, without requiring an iterative adjustment process, making it potentially much faster.

A:
A Single-Pass Classifier for Categorical Data
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