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
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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