Natural Language Processing by Reasoning and Learning Pei Wang Temple University Philadelphia, 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 raven t1978 Inheritance represent

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