scholarly journals Child-directed speech is optimized for syntax-free semantic inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanghao You ◽  
Balthasar Bickel ◽  
Moritz M. Daum ◽  
Sabine Stoll
Keyword(s):  
Structural Information ◽  
Syntactic Structure ◽  
Test Case ◽  
Semantic Inference ◽  
The Core ◽  
Text Collections ◽  
Different Types ◽  
Complex Adaptive ◽  
Core Meaning ◽  
Extract Information

AbstractThe way infants learn language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from the speech they hear and combine it with information from the external environment. Most theories assume that this ability critically hinges on the recognition of at least some syntactic structure. Here, we show that child-directed speech allows for semantic inference without relying on explicit structural information. We simulate the process of semantic inference with machine learning applied to large text collections of two different types of speech, child-directed speech versus adult-directed speech. Taking the core meaning of causality as a test case, we find that in child-directed speech causal meaning can be successfully inferred from simple co-occurrences of neighboring words. By contrast, semantic inference in adult-directed speech fundamentally requires additional access to syntactic structure. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered syntactic structure.

scholarly journals Child-directed speech is statistically optimized for meaning extraction

2020 ◽  
Author(s):  
Guanghao You ◽  
Balthasar Bickel ◽  
Moritz M. Daum ◽  
Sabine Stoll
Keyword(s):  
First Language ◽  
Structural Complexity ◽  
Written Language ◽  
Learning Mechanisms ◽  
Underlying Distribution ◽  
Different Types ◽  
Complex Adaptive ◽  
Distributional Learning ◽  
Complex Structural ◽  
Extract Information

The way infants manage to extract meaning from the speech stream when learning their first language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from speech they hear and combine it with the external environment they encounter. However, little is known about the underlying distribution of information in speech that conditions this ability. Here we examine properties of this distribution that support meaning extraction in three different types of speech: child-directed speech, adult conversation, and, as a control, written language. We find that verb meanings in child-directed speech can already be successfully extracted from simple co-occurrences of neighboring words, whereas meaning extraction in the other types of speech fundamentally requires access to more complex structural relations between neighboring words. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered the structural complexity of her language and therefore mainly relies on distributional learning mechanisms to develop an understanding of linguistic meanings.

scholarly journals Asymmetric Attributional Word Similarities Measures to detect Relations of Textual Generality

2020 ◽  
Author(s):  
Sebastião Pais ◽  
Gaël Dias
Keyword(s):  
Language Processing ◽  
Independent Method ◽  
Inference Mechanism ◽  
Semantic Inference ◽  
The Core ◽  
Annotation Data ◽  
Entailment Relation ◽  
Different Types ◽  
Textual Entailment ◽  
Task Systems

In this work we present a new unsupervised and language-independent methodology to detect relations of textual generality, for this, we introduce a particular case of textual entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE recognition (RTE) task, systems are asked to automatically judge whether the meaning of a portion of the text, the Text - T, entails the meaning of another text, the Hypothesis - H. Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signalling of renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect relations of textual generality. In-text, there are different kinds of entailment, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have T→GH whenever the premise T entails the hypothesis H, being it also more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair ⟨T,H⟩ having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is to highlight the importance of this inference mechanism. Consequently, the new annotation data seems to be a valuable resource for the community.

Political Personalization

2018 ◽  
Author(s):  
Gideon Rahat ◽  
Ofer Kenig
Keyword(s):  
Theoretical Basis ◽  
Research Literature ◽  
Party Change ◽  
The Core ◽  
Different Types ◽  
National Perspective ◽  
Definition Of ◽  
Core Meaning ◽  
Cross National

The chapter lays down the conceptual and theoretical basis for the analysis of political personalization from a cross-national perspective. It proposes a definition of political personalization and a typology of its types—institutional, media, and behavioral—and its subtypes—personalization of governmental and nongovernmental institutions; of controlled and uncontrolled media; and of the behavior of politicians and voters. It looks closely at the core meaning of the proposed definition, at its broadness, and at the implications of perceiving personalization as a process. The chapter also examines, on the basis of the research literature, the general causes of political personalization—party change, mediatization, individualization—and the relationships between its different types.

scholarly journals Phenotyping plants: genes, phenes and machines

2012 ◽  
Vol 39 (11) ◽  
pp. 813 ◽  
Author(s):  
Roland Pieruschka ◽  
Hendrik Poorter
Keyword(s):  
Structural Information ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Special Issue ◽  
Non Invasive ◽  
The Core ◽  
The Relationship ◽  
Non Destructive ◽  
Major Progress ◽  
Meaningful Data

No matter how fascinating the discoveries in the field of molecular biology are, in the end it is the phenotype that matters. In this paper we pay attention to various aspects of plant phenotyping. The challenges to unravel the relationship between genotype and phenotype are discussed, as well as the case where ‘plants do not have a phenotype’. More emphasis has to be placed on automation to match the increased output in the molecular sciences with analysis of relevant traits under laboratory, greenhouse and field conditions. Currently, non-destructive measurements with cameras are becoming widely used to assess plant structural properties, but a wider range of non-invasive approaches and evaluation tools has to be developed to combine physiologically meaningful data with structural information of plants. Another field requiring major progress is the handling and processing of data. A better e-infrastructure will enable easier establishment of links between phenotypic traits and genetic data. In the final part of this paper we briefly introduce the range of contributions that form the core of a special issue of this journal on plant phenotyping.

scholarly journals Crystal Structure of African Swine Fever Virus pS273R Protease and Implications for Inhibitor Design

2020 ◽  
Vol 94 (10) ◽  
Author(s):  
Guobang Li ◽  
Xiaoxia Liu ◽  
Mengyuan Yang ◽  
Guangshun Zhang ◽  
Zhengyang Wang ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Structural Information ◽  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Hydrolytic Activity ◽  
Viral Disease ◽  
Fever Virus ◽  
Dna Virus ◽  
Core Domain ◽  
The Core

ABSTRACT African swine fever (ASF) is a highly contagious hemorrhagic viral disease of domestic and wild pigs that is responsible for serious economic and production losses. It is caused by the African swine fever virus (ASFV), a large and complex icosahedral DNA virus of the Asfarviridae family. Currently, there is no effective treatment or approved vaccine against the ASFV. pS273R, a specific SUMO-1 cysteine protease, catalyzes the maturation of the pp220 and pp62 polyprotein precursors into core-shell proteins. Here, we present the crystal structure of the ASFV pS273R protease at a resolution of 2.3 Å. The overall structure of the pS273R protease is represented by two domains named the “core domain” and the N-terminal “arm domain.” The “arm domain” contains the residues from M1 to N83, and the “core domain” contains the residues from N84 to A273. A structure analysis reveals that the “core domain” shares a high degree of structural similarity with chlamydial deubiquitinating enzyme, sentrin-specific protease, and adenovirus protease, while the “arm domain” is unique to ASFV. Further, experiments indicated that the “arm domain” plays an important role in maintaining the enzyme activity of ASFV pS273R. Moreover, based on the structural information of pS273R, we designed and synthesized several peptidomimetic aldehyde compounds at a submolar 50% inhibitory concentration, which paves the way for the design of inhibitors to target this severe pathogen. IMPORTANCE African swine fever virus, a large and complex icosahedral DNA virus, causes a deadly infection in domestic pigs. In addition to Africa and Europe, countries in Asia, including China, Vietnam, and Mongolia, were negatively affected by the hazards posed by ASFV outbreaks in 2018 and 2019, at which time more than 30 million pigs were culled. Until now, there has been no vaccine for protection against ASFV infection or effective treatments to cure ASF. Here, we solved the high-resolution crystal structure of the ASFV pS273R protease. The pS273R protease has a two-domain structure that distinguishes it from other members of the SUMO protease family, while the unique “arm domain” has been proven to be essential for its hydrolytic activity. Moreover, the peptidomimetic aldehyde compounds designed to target the substrate binding pocket exert prominent inhibitory effects and can thus be used in a potential lead for anti-ASFV drug development.

scholarly journals Marked and Non-Marked Attribute Structures within a Secondary Predicate in the French Language

2020 ◽  
pp. 143-155
Author(s):  
Iuliia Zelena
Keyword(s):  
Information Structure ◽  
Syntactic Structure ◽  
French Language ◽  
Different Types ◽  
The Status ◽  
Productive Potential ◽  
Semantic Transfer ◽  
Argumentative Structure ◽  
Transfer Mechanisms

The article is devoted to the study of marked and unmarked attributive constructions, considering the influence of semantic transfer mechanisms, taking into account their productive potential. Structuraland semantic characterization of types of attributive constructions is performed in light of two initial theories –in view ofreferentstatusand actual sentence fragmentation. An independent research of relationsbetween predication manifestations and the semanticand syntactic structure of a sentence has been performed by determining the valence properties of attributive verbs. The distinctionbetween different types of constructionswith the attribute complement isdescribed and it is specifiedthat there is a direct correlation between the type of attributive sentences and the emergence of predicative relation.The article is dedicated to the analysis of modification of the information structure and the status of the referent in sentences containing attributive object. Given the types of interpretation of attributive sentences and, based on the results of contextual analysis, it became possible to prove that change of argumentative structure in verbal group gives a reason to treat attributive verb with elements SN2 and X as a predicative focus of the sentence and permit to consider it as the complex secondary predicate.

scholarly journals A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution

2022 ◽  
Vol 16 (4) ◽  
pp. 1-16
Author(s):  
Fereshteh Jafariakinabad ◽  
Kien A. Hua
Keyword(s):  
Structural Information ◽  
Syntactic Structure ◽  
Representation Learning ◽  
Authorship Attribution ◽  
Sentence Structure ◽  
Vector Representation ◽  
Writing Style ◽  
Neural Models ◽  
Syntactic Information ◽  
Classification Tasks

The syntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different downstream tasks across many domains. Even though utilizing probing methods in several studies suggests that these learned contextual representations implicitly encode some amount of syntax, explicit syntactic information further improves the performance of deep neural models in the domain of authorship attribution. These observations have motivated us to investigate the explicit representation learning of syntactic structure of sentences. In this article, we propose a self-supervised framework for learning structural representations of sentences. The self-supervised network contains two components; a lexical sub-network and a syntactic sub-network which take the sequence of words and their corresponding structural labels as the input, respectively. Due to the n -to-1 mapping of words to their structural labels, each word will be embedded into a vector representation which mainly carries structural information. We evaluate the learned structural representations of sentences using different probing tasks, and subsequently utilize them in the authorship attribution task. Our experimental results indicate that the structural embeddings significantly improve the classification tasks when concatenated with the existing pre-trained word embeddings.

scholarly journals Kiljan 'Stone stuck into the ground': A dalmato-romance relic in Montenegro?

2009 ◽  
pp. 89-99
Author(s):  
Aleksandar Loma
Keyword(s):  
19Th Century ◽  
Building Block ◽  
Arable Land ◽  
Violent Death ◽  
Romance Language ◽  
The Core ◽  
Latin Word ◽  
Mediterranean Countries ◽  
Etymological Dictionary ◽  
Core Meaning

The word kiljan / kiljan, -a (Variants: kiljen, kiljas) is found in the most parts of Montenegro; its area ranges over the border between Zeta - and East-Herzegovina dialects of Serbian. Of its five meanings, three are to be considered peripheral (building block (of limestone) in SW, target in a game in NW), or occasional (hill). The core meaning of the word seem to be 'a stone stuck into the ground', to mark something, either a boundary between the fields or a place of somebody's violent death (shifting to 'gravestone'). Of these two usages, the former may claim the priority, ancient boundary stones being often reinterpreted, in local legends, as memorial ones. Indeed marking the land parcels with stones was unfamiliar to the ancient Slavs (Common Slavic *medja land boundary is usually a hedge, a grove, a path or a furrow), but characteristic of Mediterranean countries with their scarcity of arable land; for the ancient Greeks, it is attested since the Homeric epoch, and was practiced by the Romans too, which suggests a possible Romance source of the word in question. Significantly enough, this practice is attested by the Old Serbian charters only for Zeta, a SW Montenegrian region where kiljan is the proper term for this kind of landmarks (in a charter from 1316, it is not explicitly mentioned, but described by kamy ukopan stone dug into the ground). The word kiljan has no convincing etymology so far Illyrian one proposed by Petar Skok in his etymological dictionary is made up out of thin air, and a possible interpretation based on (Balto)Slavic facts (Lith. ku?lis 'stone', Common Slavic **kyl- as a variant of *k?l- eyetooth tusk; crag') highly improbable as well. However, the Old Dalmatian, a Romance language extinct since the end of 19th century, provides a plausible source with its continuation of the Latin word columna 'column, pillar', which is kilauna; and the SCr forms kelomna / kelovna in Ragusa (Dubrovnik), kilovna in the Bay of Cattaro (Kotor) in today's Montenegro must go back to a similar Romance form. Although the details of vocalism are not clear, especially the development in the penultimate, the derivation kiljan columna seems highly probable in view of the fact that in medieval Latin sources from Dalmatia the same thing - a boundary stone - is designated by colonella, a derivative from Lat. columna.

Structure within Morphemic Meaning

2021 ◽  
Vol 7 (2) ◽  
pp. 155-231
Author(s):  
Leonard Talmy
Keyword(s):  
Conceptual Content ◽  
The Core ◽  
Core Meaning

Abstract The entire conceptual content represented by a single morpheme—its plenary meaning—is in general both copious and structured. This structuring consists of both the patterning of its content and the distribution of attention over that pattern. With respect to the patterning of its content, a morpheme’s plenary meaning can be divided into a core meaning and an associated meaning. In turn, its associated meaning can be subdivided into five sectors: the holistic, infrastructure, collateral, disposition, and attitude sectors. And with respect to its distribution of attention, eight specific attentional factors and three general attentional principles are cited. The main attentional factor is that a morpheme’s core meaning is generally more salient than its associated meaning or any of the sectors therein. But another attentional factor holds that the attitude sector, especially its expletivity type, can challenge or exceed the core meaning in salience.

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