31,110 research outputs found
The Learning-Knowledge-Reasoning Paradigm for Natural Language Understanding and Question Answering
Given a text, several questions can be asked. For some of these questions, the answer can be directly looked up from the text. However for several other questions, one might need to use additional knowledge and sophisticated reasoning to find the answer. Developing AI agents that can answer these kinds of questions and can also justify their answer is the focus of this research. Towards this goal, we use the language of Answer Set Programming as the knowledge representation and reasoning language for the agent. The question then arises, is how to obtain the additional knowledge? In this work we show that using existing Natural Language Processing parsers and a scalable Inductive Logic Programming algorithm it is possible to learn this additional knowledge (containing mostly commonsense knowledge) from question-answering datasets which then can be used for inference
An Application of Fuzzy Inductive Logic Programming for Textual Entailment and Value Mining
The aim of this preliminary report is to give an overview of textual entailment in natural language processing (NLP), to present our approach to research and to explain the possible applications for such a system. Our system presupposes several modules, namely the sentiment analysis module, the anaphora resolution module, the named entity recognition module and the relationship extraction module. State-of-the-art modules will be used but no amount of research will go into this. The research focuses on the main module that extracts background knowledge from the extracted relationships via resolution and inverse resolution (inductive logic programming). The last part focuses on possible economic applications of our research
LLMs for Relational Reasoning: How Far are We?
Large language models (LLMs) have revolutionized many areas (e.g. natural
language processing, software engineering, etc.) by achieving state-of-the-art
performance on extensive downstream tasks. Aiming to achieve robust and general
artificial intelligence, there has been a surge of interest in investigating
the reasoning ability of the LLMs. Whereas the textual and numerical reasoning
benchmarks adopted by previous works are rather shallow and simple, it is hard
to conclude that the LLMs possess strong reasoning ability by merely achieving
positive results on these benchmarks. Recent efforts have demonstrated that the
LLMs are poor at solving sequential decision-making problems that require
common-sense planning by evaluating their performance on the reinforcement
learning benchmarks. In this work, we conduct an in-depth assessment of several
state-of-the-art LLMs' reasoning ability based on the inductive logic
programming (ILP) benchmark, which is broadly recognized as a representative
and challenging measurement for evaluating logic program induction/synthesis
systems as it requires inducing strict cause-effect logic to achieve robust
deduction on independent and identically distributed (IID) and
out-of-distribution (OOD) test samples. Our evaluations illustrate that
compared with the neural program induction systems which are much smaller in
model size, the state-of-the-art LLMs are much poorer in terms of reasoning
ability by achieving much lower performance and generalization using either
natural language prompting or truth-value matrix prompting.Comment: Accepted by The First International Workshop on Large Language Models
for Code (ICSE 2024
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources
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