Search Results for "structure"

...Instead of asking a language model for a single direct translation, it first generates a translation, then asks the model to critique it, and finally uses that critique to produce a stronger version. This structure makes the system more steerable than a traditional translation pipeline. Users can adjust prompts to control tone, formality, terminology, idiom handling, and regional language choices. The project includes example scripts and a simple Python interface for translating between source and target languages. It is useful for researchers, developers, and localization teams exploring how LLM-based workflows can produce better, more customizable translations.

LANGANA-E is an English natural language parser. It is the main structure that automatic understanding will be built upon. Automatic understanding can be used for answering questions automatically or finding an answer from a reference text archive.

HermeneutiX is a tool for diagramming syntactic and semantic structures of complex (not necessarily foreign-language) texts (e.g. bible or other historical excerpts). HermeneutiX is now part of SciToS (the scientific tool set). Starting with version 2.0.0, HermeneutiX can be found on GitHub. Please check out the release summary: https://github.com/scientific-tool-set/scitos/releases For an introduction, check out this video: https://youtu.be/uQjewyG0Ad8 PS: To run a Java...

...The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). Then, the summarizer gets trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). This method can be used to create the user-defined classes by merging texts of certain categories and then to calculate the relevant distances between the documents, but this is not necessary.

An agent-based situated language learning simulation that focuses on lexical learning and grounding, featuring a unigram syntax structure and a CFG-based semantic grammar. Created as a MSc thesis project, using python.