Project details for Aika

Logo Aika 0.8

by molzberger - September 19, 2017, 18:10:43 CET [ Project Homepage BibTeX Download ]

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Description:

Aika is a Java library that automatically extracts and annotates semantic information into text. In case this information is ambiguous, Aika will generate several hypothetical interpretations concerning the meaning of the text and pick the most likely one. The Aika algorithm is based on various ideas and approaches from the field of AI such as artificial neural networks, frequent pattern mining and logic based expert systems. It can be applied to a broad spectrum of text analysis task and combines these concepts in a single algorithm.

Aika allows to model linguistic concepts like words, word meanings (entities), categories (e.g. person name, city), grammatical word types and so on as neurons in a neural network. By choosing appropriate synapse weights, these neurons can take on different functions within the network. For instance neurons whose synapse weights are chosen to mimic a logical AND can be used to match an exact phrase. On the other hand neurons with an OR characteristic can be used to connect a large list of word entity neurons to determine a category like 'city' or 'profession'.

Aika is based on non-monotonic logic, meaning that it first draws tentative conclusions only. In other words, Aika is able to generate multiple mutually exclusive interpretations of a word, phrase, or sentence, and select the most likely interpretation. For example a neuron representing a specific meaning of a given word can be linked through a negatively weighted synapse to a neuron representing an alternative meaning of this word. In this case these neurons will exclude each other. These synapses might even be cyclic. Aika can resolve such recurrent feedback links by making tentative assumptions and starting a search for the highest ranking interpretation.

In contrast to conventional neural networks, Aika propagates activations objects through its network, not just activation values. These activation objects refer to a text segment and an interpretation.

Aika consists of two layers. The neural layer, containing all the neurons and continuously weighted synapses and underneath that the discrete logic layer, containing a boolean representation of all the neurons. The logic layer uses a frequent pattern lattice to efficiently store the individual logic nodes. This architecture allows Aika to process extremely large networks since only neurons that are activated by a logic node need to compute their weighted sum and their activation value. This means that the fast majority of neurons stays inactive during the processing of a given text.

To prevent that the whole network needs to stay in memory during processing, Aika uses the provider pattern to suspend individual neurons or logic nodes to an external storage like a mongo db.

Changes to previous version:

Aika Version 0.8 (2017-09-17) - Optimization of the interpretation search using an upper bound on the interpretation weights. - Support for very large models with millions of neurons by suspending rarely used neurons to disk.

Aika Version 0.7 (2017-08-06) - Refactoring of the range model. Now the range begin and the range end can be treated independently of each other. Synapses now have three properties: range match, range output and range mapping. - The Iteration class has been merged into the document class. - Performance optimizations for the interpretation search in the SearchNode class. - Test case fixes - Class renaming: Option -> InterprNode, ExpandNode -> SearchNode - Lots of javadoc

Aika Version 0.6 (2017-07-01) - Mainly optimizations

BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Platform Agnostic
Data Formats: Txt
Tags: Information Extraction, Inference, Neural Network, Text Mining
Archive: download here

Other available revisons

Version Changelog Date
0.8

Aika Version 0.8 (2017-09-17) - Optimization of the interpretation search using an upper bound on the interpretation weights. - Support for very large models with millions of neurons by suspending rarely used neurons to disk.

Aika Version 0.7 (2017-08-06) - Refactoring of the range model. Now the range begin and the range end can be treated independently of each other. Synapses now have three properties: range match, range output and range mapping. - The Iteration class has been merged into the document class. - Performance optimizations for the interpretation search in the SearchNode class. - Test case fixes - Class renaming: Option -> InterprNode, ExpandNode -> SearchNode - Lots of javadoc

Aika Version 0.6 (2017-07-01) - Mainly optimizations

September 19, 2017, 18:10:43
0.5

Initial Announcement on mloss.org.

June 9, 2017, 20:22:20

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