Natural Language ProcessingNatural Language Processingby Reasoning and Learningby Reasoning and Learning

Pei WangTemple UniversityPhiladelphia, USA

NLP in NARS: basics

To represent linguistic knowledge in the same form as other knowledge

To derive linguistic knowledge from the system's experience using inference rules

To treat language understanding and production as reasoning

There is no separate “NLP module”

Knowledge Representation

A term names a concept in the system A term may correspond to a sensation, an

action, or a word in a language A term may be a compound formed from

other terms Two terms linked by a copula forms a

statement indicating their substitutability

Experience-Grounded Semantics

The truth-value of a statement is a pair, ‹frequency, confidence›, in [0, 1] x (0, 1) that indicating its evidential support

Frequency is the proportion of positive evidence among all evidence; confidence is the proportion of available evidence among evidence at an evidential horizon

The meaning of a term is its experienced relation with the other terms

Inference Rules

NARS has rules for various types of inference, including deduction, induction, abduction, revision, choice, comparison, analogy, compound composition, etc.

Each inference rule has a truth-value function that calculates the evidence provided by the premises to the conclusion

Rules can be strong or weak, w.r.t. the confidence of the conclusion

Memory and Control

NARS is based on the assumption of insufficient knowledge and resources, i.e., the system has finite capacity, works in real time, and is open to unanticipated tasks

When processing a task, the system only selectively uses its knowledge, and each concept involved only uses partial meaning

The tasks are processed case by case

Memory as a Network

t135

t8762

bird

chicken

t8734

鸟

鸡

乌鸦crow

ravent1978

Inheritancerepresent

Architecture and Work Cycle

An Example[1, input] {cat * cat} → represent ‹1, 0.9› [2, input] {fish * fish} → represent ‹1, 0.9› (2)[3, input] {{cat * eat* fish} * ((cat * fish) → food)} → represent ‹1, 0.9› [4, induction from 1&3] ({$1 * $2} → represent) ⇒({{$1 * eat* fish} *(($2 * fish ) → food )} → represent) ‹1, 0.45› [5, induction from 2&4] (({$1 * $2} → represent) ∧ ({$3 * $4} → represent )) ⇒({{$1 * eat * $3} * (($2 * $4) → food)} → represent) ‹1, 0.29›[6, input] {dog * dog} → represent ‹1, 0.9›[7, input] {meat * meat} → represent ‹1, 0.9›[8, deduction from 4&6] {{dog * eat *fish} * ((dog * fish) → food)} → represent ‹1, 0.41›[9, deduction from 5&7] {{dog * eat * meat}* ((dog * meat) → food)} → represent ‹1, 0.26›

Features

Unified treatment of syntax, semantics, and pragmatics

Do not depends on a given grammar or grammatical categories, and represent grammatical knowledge at multiple levels

Learning is on-line, one-shot, incremental, life-long, and is carried out by reasoning

To treat meaning as experience-grounded and context-sensitive

Summary

Both grammar and vocabulary can be learned from the experience of the system

The meaning of a word should be determined by experience, rather than by denotation or definition

It is possible to carry out NLP by a unified reasoning-learning mechanism, rather than by a separate module