Search Results for author:
Found 9 papers, 5 papers with code
Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem
We show the effectiveness of our design via Natlog apps working as orchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL. E prompt generators.
Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles
We automate deep step-by step reasoning in an LLM dialog thread by recursively exploring alternatives (OR-nodes) and expanding details (AND-nodes) up to a given depth.
A Gaze into the Internal Logic of Graph Neural Networks, with Logic
The problem is known as graph node property prediction and our approach will consist in emulating with help of a Prolog program the key information propagation steps of a Graph Neural Network's training and inference stages.
Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch
We introduce Natlog, a lightweight Logic Programming language, sharing Prolog's unification-driven execution model, but with a simplified syntax and semantics.
Deriving Theorems in Implicational Linear Logic, Declaratively
Keywords: combinatorial generation of provable formulas of a given size, intuitionistic and linear logic theorem provers, theorems of the implicational fragment of propositional linear intuitionistic logic, Curry-Howard isomorphism, efficient generation of linear lambda terms in normal form, Prolog programs for lambda term generation and theorem proving.
Interactive Text Graph Mining with a Prolog-based Dialog Engine
Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements.
Dependency-based Text Graphs for Keyphrase and Summary Extraction with Applications to Interactive Content Retrieval
We build a bridge between neural network-based machine learning and graph-based natural language processing and introduce a unified approach to keyphrase, summary and relation extraction by aggregating dependency graphs from links provided by a deep-learning based dependency parser.
TextRank: Bringing Order into Texts
In this paper, we introduce TextRank – a graph-based ranking model for text processing and show how this model can be successfully used in natural language applications.
