[Paper Review] Modeling meaning: computational interpreting and understanding of natural language fragments

Michael Kapustin, Pavlo Kapustin|arXiv (Cornell University)|May 29, 2015

Natural Language Processing Techniques4 citations

TL;DR

This paper presents a novel computational framework for modeling the meaning of natural language fragments through a compositional, context-sensitive knowledge representation that encodes quantitative semantic information. It enables interpretable understanding of ambiguous or incomplete language by detecting semantic incomprehension and selecting the most contextually plausible interpretation, with a focus on handling uncertainty and enabling dynamic knowledge integration.

ABSTRACT

In this introductory article we present the basics of an approach to implementing computational interpreting of natural language aiming to model the meanings of words and phrases. Unlike other approaches, we attempt to define the meanings of text fragments in a composable and computer interpretable way. We discuss models and ideas for detecting different types of semantic incomprehension and choosing the interpretation that makes most sense in a given context. Knowledge representation is designed for handling context-sensitive and uncertain / imprecise knowledge, and for easy accommodation of new information. It stores quantitative information capturing the essence of the concepts, because it is crucial for working with natural language understanding and reasoning. Still, the representation is general enough to allow for new knowledge to be learned, and even generated by the system. The article concludes by discussing some reasoning-related topics: possible approaches to generation of new abstract concepts, and describing situations and concepts in words (e.g. for specifying interpretation difficulties).

Motivation & Objective

• To develop a computationally interpretable method for modeling word and phrase meanings in context.
• To address semantic incomprehension in natural language by detecting and resolving ambiguity through contextual reasoning.
• To design a knowledge representation that supports context-sensitive, uncertain, and imprecise information with extensibility for new knowledge.
• To enable the system to generate or learn new abstract concepts and describe interpretation difficulties in natural language.
• To support reasoning and interpretation in natural language understanding beyond static semantic dictionaries.

Proposed method

• The approach uses a compositional knowledge representation that captures quantitative essence of concepts, enabling formal reasoning over meaning.
• It models context sensitivity by dynamically adjusting interpretations based on contextual constraints and available information.
• Semantic incomprehension is detected by analyzing inconsistencies or lack of coherence in interpretation attempts.
• The system selects the most plausible interpretation by evaluating compatibility with context and prior knowledge.
• The representation supports incremental learning and accommodation of new information without retraining.
• Abstract concepts are generated through structural and semantic composition of existing knowledge components.

Experimental results

Research questions

• RQ1 How can natural language fragments be interpreted in a way that is both computationally interpretable and context-sensitive?
• RQ2 What mechanisms can detect and resolve semantic incomprehension in ambiguous or incomplete language?
• RQ3 How can a knowledge representation model handle uncertainty and imprecise information in natural language understanding?
• RQ4 In what ways can a system generate or learn new abstract concepts through composition of existing knowledge?
• RQ5 How can interpretation difficulties be described and diagnosed in natural language terms?

Key findings

• The framework enables computational interpretation of natural language fragments by modeling meaning through a context-aware, quantitative knowledge representation.
• Semantic incomprehension is systematically detected by evaluating coherence and consistency of interpretation candidates.
• The system selects the most contextually plausible interpretation through a formalized reasoning process over stored semantic knowledge.
• The knowledge representation supports dynamic integration of new information and adaptation to novel contexts.
• The approach allows for the generation of new abstract concepts through composition of existing semantic components.
• Interpretation difficulties are expressible in natural language, enabling system self-description of reasoning challenges.