scholarly journals Probabilistic Type Theory and Natural Language Semantics

2015 ◽  
Vol 10 ◽  
Author(s):  
Robin Cooper ◽  
Simon Dobnik ◽  
Shalom Lappin ◽  
Staffan Larsson
Keyword(s):  
Natural Language ◽  
Programming Languages ◽  
Type Theory ◽  
Probabilistic Reasoning ◽  
Syntactic Structure ◽  
Formal Semantics ◽  
Semantic Interpretation ◽  
Semantic Learning ◽  
History Of ◽  
Learning And Cognition

Type theory has played an important role in specifying the formal connection between syntactic structure and semantic interpretation within the history of formal semantics. In recent years rich type theories developed for the semantics of programming languages have become influential in the semantics of natural language. The use of probabilistic reasoning to model human learning and cognition has become an increasingly important part of cognitive science. In this paper we offer a probabilistic formulation of a rich type theory, Type Theory with Records (TTR), and we illustrate how this framework can be used to approach the problem of semantic learning. Our probabilistic version of TTR is intended to provide an interface between the cognitive process of classifying situations according to the types that they instantiate, and the compositional semantics of natural language.

Dependency Parsing

2011 ◽  
pp. 449-455
Author(s):  
Ruket Çakici
Keyword(s):  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Syntactic Structure ◽  
Semantic Interpretation ◽  
Dependency Parsing ◽  
Natural Languages ◽  
Functional Representation ◽  
Statistical Dependency ◽  
Direct Dependency

Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for machine learning systems for a wide variety of applications from Parsing to Machine Translation (Quirk et al., 2005). Dependency representation is preferred for many languages because linguistic and semantic information is easier to retrieve from the more direct dependency representation. Dependencies are relations that are defined on words or smaller units where the sentences are divided into its elements called heads and their arguments, e.g. verbs and objects. Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation or syntactic structure. Parsing is usually considered as the first step of Natural Language Processing (NLP). To train statistical parsers, a sample of data annotated with necessary information is required. There are different views on how informative or functional representation of natural language sentences should be. There are different constraints on the design process such as: 1) how intuitive (natural) it is, 2) how easy to extract information from it is, and 3) how appropriately and unambiguously it represents the phenomena that occur in natural languages. In this article, a review of statistical dependency parsing for different languages will be made and current challenges of designing dependency treebanks and dependency parsing will be discussed.

Dependency Parsing

2012 ◽  
pp. 2117-2124
Author(s):  
Ruket Çakici
Keyword(s):  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Syntactic Structure ◽  
Semantic Interpretation ◽  
Dependency Parsing ◽  
Natural Languages ◽  
Functional Representation ◽  
Statistical Dependency ◽  
Direct Dependency

Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for machine learning systems for a wide variety of applications from Parsing to Machine Translation (Quirk et al., 2005). Dependency representation is preferred for many languages because linguistic and semantic information is easier to retrieve from the more direct dependency representation. Dependencies are relations that are defined on words or smaller units where the sentences are divided into its elements called heads and their arguments, e.g. verbs and objects. Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation or syntactic structure. Parsing is usually considered as the first step of Natural Language Processing (NLP). To train statistical parsers, a sample of data annotated with necessary information is required. There are different views on how informative or functional representation of natural language sentences should be. There are different constraints on the design process such as: 1) how intuitive (natural) it is, 2) how easy to extract information from it is, and 3) how appropriately and unambiguously it represents the phenomena that occur in natural languages. In this article, a review of statistical dependency parsing for different languages will be made and current challenges of designing dependency treebanks and dependency parsing will be discussed.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

‘Struggle, Not Destiny’

2015 ◽  
Vol 23 (2) ◽  
pp. 157-175
Author(s):  
Panagiotis Sotiris
Keyword(s):  
Practical Reason ◽  
Probabilistic Reasoning ◽  
History Of Mathematics ◽  
Starting Point ◽  
History Of ◽  
History Of Sciences

On the occasion of the publication of the translation of Pierre Raymond’s text on Althusser’s materialism, we attempt an introduction to his theoretical trajectory. We begin with his conception of the conflict between materialism and idealism inLe passage au matérialismein 1973 and his thinking on the question of the history of sciences inL’histoire & les sciences(1975), before turning our attention to his elaboration on the question of a history of mathematics and in particular of the emergence of probabilistic reasoning. Then we examine his confrontation with the question of the relation between materialism and dialectics inMatérialisme dialectique et logique. After that, we proceed to his conception of the need to break with any form of teleology and finalism as the starting point for a new conception of causality (La résistible fatalité de l’histoireof 1982), before looking atDissiper la terreur et les ténèbresof 1992 and his attempt to rethink the question of practical reason. It is in light of the above presentation that we insist on the importance of Raymond’s text on Althusser.

Towards Probabilistic Reasoning in Type Theory - The Intersection Type Case

2020 ◽  
pp. 122-139
Author(s):  
Silvia Ghilezan ◽  
Jelena Ivetić ◽  
Simona Kašterović ◽  
Zoran Ognjanović ◽  
Nenad Savić
Keyword(s):  
Type Theory ◽  
Probabilistic Reasoning

scholarly journals On Learning Interpreted Languages with Recurrent Models

2022 ◽  
pp. 1-13
Author(s):  
Denis Paperno
Keyword(s):  
Natural Language ◽  
Data Processing ◽  
Syntactic Structure ◽  
Neural Nets ◽  
Training Data ◽  
Sequential Data ◽  
Extensive Training ◽  
Formal Syntax ◽  
Compositional Interpretation

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

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