Natural language syntax complies with the free-energy principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating elementary syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” and “least effort” criteria from theoretical syntax—adhere to the FEP. This permits a greater degree of explanatory power to the FEP—with respect to higher language functions—and presents linguists with a grounding in first principles of notions pertaining to computability. More generally, we explore the possibility of migrating certain topics in linguistics over to the domain of fields that investigate the FEP, such as complex polysemy. We aim to align concerns of linguists with the normative model for organic self-organisation associated with the FEP, marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

Subcortical syntax: Reconsidering the neural dynamics of language

Subcortical contributions to core linguistic computations (pertaining to syntax-semantics) remain drastically under-studied. We critique the cortico-centric focus which has largely accompanied research into these higher-order linguistic functions and suggest that, while much remains unknown, there is nevertheless a rich body of knowledge concerning the possible role of subcortex in language. We ultimately attempt to show that there is a clear place for subcortex in models of natural language syntax-semantics, including a role in binary set-formation, categorized object maintenance, lexico-semantic processing, morphosyntactic linearization, and cross-cortical representational integration. In particular, we consult models of language processing relying on oscillatory brain dynamics in order to investigate both the apparent and possible functional roles of subcortex in language.

The neural basis of combinatory syntax and semantics

Human language allows us to create an infinitude of ideas from a finite set of basic building blocks. What is the neurobiology of this combinatory system? Research has begun to dissect the neural basis of natural language syntax and semantics by analyzing the basics of meaning composition, such as two-word phrases. This work has revealed a system of composition that involves rapidly peaking activity in the left anterior temporal lobe and later engagement of the medial prefrontal cortex. Both brain regions show evidence of shared processing between comprehension and production, as well as between spoken and signed language. Both appear to compute meaning, not syntactic structure. This Review discusses how language builds meaning and lays out directions for future neurobiological research on the combinatory system.

Syntax

This chapter introduces some of the phenomena that theories of natural-language syntax aim to account for. It briefly discusses the correspondence between the sentences of a language and the semantic predicate-argument relations that they express, indicating how that correspondence is encoded in terms of word order, phrase structure, agreement, and valence. It surveys some of the grammatical notations, syntactic representations, and theoretical approaches that have figured prominently in linguistic research and that have particularly influenced the development of natural-language processing algorithms and implementations.

Grammatical category influences lateralized imagery for sentences

abstractNatural language syntax has previously been thought to reflect abstract processing rules independent of meaning construction. However, grammatical categories may serve a functional role by allocating attention towards recurrent topics in discourse. Here, we show that listeners incorporate grammatical category into imagery when producing stick figure drawings from heard sentences, supporting the latter view. Participants listened to sentences with transitive verbs that independently varied whether a male or a female character (1) was mentioned first, (2) was the agent or recipient of an action, and (3) was the grammatical subject or object of the sentence. Replicating previous findings, we show that the first named character as well as the agent of the sentence tends to be drawn to the left in the image, probably reflecting left-to-right reading direction. But we also find that the grammatical subject of the sentence has a propensity to be drawn to the left of the object. We interpret this to suggest that grammatical category carries discursive meaning as an attention allocator. Our findings also highlight how language influences processes hitherto thought to be non-linguistic.

Semi-Automated Ontology building through Natural Language Processing

Ontology is a backbone of semantic web which is used for domain knowledge representation. Ontology provides the platform for effective extraction of information. Usually, ontology is developed manually, but the manual ontology construction requires lots of efforts by domain experts. It is also time consuming and costly. Thus, an approach to build ontology in semi-automated manner has been proposed. The proposed approach extracts concept automatically from open directory Dmoz. The Stanford Parser is explored to parse natural language syntax and extract the parts of speech which are used to form the relationship among the concepts. The experimental result shows a fair degree of accuracy which may be improved in future with more sophisticated approach.