Compositionality and Merger Representations

Default Semantics (DS) is a theory of discourse that represents the main meaning intended by the speaker and recovered by the addressee, using truth-conditional, formal, but pragmatics-rich representations. It was originally developed at the University of Cambridge by K. M. Jaszczolt in the late 1990s and has since been applied to a variety of constructions, phenomena, and languages. The category of primary meaning, as it is understood in DS and represented in its semantic qua conceptual representations, cuts across the explicit/implicit divide. Semantic representations are not limited by the constraints imposed by the logical form of the sentence; they allow for its modifications but also, unlike in other post-Gricean theories, they allow for it to be overridden when the main informational content is conveyed through sources other than the linguistic expression itself. DS identifies five sources of information, all operating on an equal footing: word meaning and sentence structure (WS); situation of discourse (SD); properties of human inferential system (IS); stereotypes and presumptions about society and culture (SC); and world knowledge (WK). Since all of the sources can contribute to the truth-conditional representation, the traditional syntactic constraint that ties the representation to the logical form of the uttered sentence could be abandoned, resulting in modeling a cognitively plausible, main message as intended by a Model Speaker and recovered by a Model Addressee. As a result, DS-theoretic representations can pertain either to (i) the logical form of the sentence; (ii) the logical form with saturated indexical expressions; (iii) the logical form that is freely modified; as well as (iv) representations that do not make use of the logical form of the sentence. The identified sources of information are mapped onto four types of processes that interact in producing the representation (called merger representation, or Σ): processing of word meaning and sentence structure (WS); conscious pragmatic inference (CPI); cognitive defaults (CD, capturing strong informativeness, or strong intentionality of the underlying mental states, for example referential rather attributive reading of definite descriptions); and social, cultural, and world knowledge defaults (SCWD, capturing the relevant sociocultural conventions and encyclopedic knowledge). “Defaults” are understood there as automatic interpretations, “shortcuts through conscious inference” for the speaker and for the context, and as such are by definition not cancellable and are immune to controversies engendered by the “noncism”-“defaultism” debates in post-Gricean pragmatics. DS subscribes to the methodological and ontological assumption of compositionality of meaning on the level of such cognitive representations (Σs).

Geographic concept is spatial objects on or near the surface of the earth and they are typically complex. Semantic relation is an important problem in the description of concept. Attribute method is widely used to realize concepts' semantic representation in a great many subjects. According to the definition of geographic concept, we use ontology property to describe geographic concept instead of ordinary language description. Through analyzing the ontology property of all geographic concepts, a system of ontology property is formed to manage all ontology properties of geographic concept. Using concept lattice, the concepts' semantic relation can be expressed clearly. We give an example of few concepts in order to validate the correctness at last.

A variety of applications have been built in recent years with the aim to extract knowledge from Al-Quran. Current knowledge representations of Al-Quran give attention primarily on conceptual ontology models that describe the semantic relations between the Quranic concepts or entities. There seems to be minimal effort towards recognizing the semantic relations between words in Quranic text, which is relatively more complex. This paper aims to present a framework for semantic knowledge representation of Al-Quran using dependency relations between words, in an attempt to boost the retrieval accuracy for Al-Quran. The semantic analysis is performed on Quranic verses according to word dependency relations using dependency parsing. Based on parsed dependencies, a set of rules are formulated to build a semantic graph of Surah Ali Imran of Al-Quran. The efficiency of the semantic representation was tested by developing a prototype question answering system. The framework was evaluated using precision and recall, First Hit Success, First Answer Reciprocal Rank and Total Reciprocal Rank by comparing the retrieved and actual answers. The results indicate that the performance of the proposed framework using word dependencies improves the semantic representation of knowledge.

The current study investigates the grammatical, rhetorical, and semantic relationship between the introduction of Surat As-Saffat and the textual structures forming the Surah. It assumes that the introduction verses are richly loaded with lexical and grammatical meanings. The title of the study, “Grammatical and Semantic Relationship between the Introduction of Surat As-Saffat and Its Textual Structures: A study in Light of the textual science,” seeks to answer the main question: (1) What are the grammatical and semantic relationship between the introduction verses of this Surah and its subsequent textual structures? This inquiry is further divided into key questions, such as: How are grammatical relationships manifested? (2) How are semantic cohesive relationships established? To answer these questions, the researcher adopted the textual methodology, which offers a theoretical framework for understanding the text as a cohesive grammatical and semantic unit, with an approach encompassing both theoretical and applied dimensions. The study reached several results, the most important of which were horizontal and vertical relationships, including repetition of grammatical style, news styles, and construction styles, in addition to frequent repetition of emphasis, forming a horizontal grammatical relationship noted by the researcher. As for semantic world and sentence structure, the text forms an interconnected semantic world through generative sentences rich in grammatical transformation; key transformational elements include the imperative, the interrogative, and emphasis, linking deep generative sentences with superficial transformational sentences.

Large-scale action recognition and video categorization are important problems in computer vision. To address these problems, we propose a novel object-and scene-based semantic fusion network and representation. Our semantic fusion network combines three streams of information using a three-layer neural network: (i) frame-based low-level CNN features, (ii) object features from a state-of-the-art large-scale CNN object-detector trained to recognize 20K classes, and (iii) scene features from a state-of-the-art CNN scene-detector trained to recognize 205 scenes. The trained network achieves improvements in supervised activity and video categorization in two complex large-scale datasets - ActivityNet and FCVID, respectively. Further, by examining and back propagating information through the fusion network, semantic relationships (correlations) between video classes and objects/scenes can be discovered. These video class-object/video class-scene relationships can in turn be used as semantic representation for the video classes themselves. We illustrate effectiveness of this semantic representation through experiments on zero-shot action/video classification and clustering.

International audience

This study aims to describe the actualization of semantic field theory in the reinterpretation of the hadiths of Islamic economic ethics. This research uses descriptive interpretive method. Actualization of semantic field theory can be explained when want to know the meaning of a word then must know the meaning of the word associated with the previous word. If viewed from the nature of the semantic relationship, then the words are grouped into a semantic field divided into 1). sintagmatic 2). set field (paradigmatic). The results of this study explain 1). Hadiths about Islamic economic ethics narrated by Ahmad and Abu Dawud with the word focus "syafā'at". contains the meaning of "mediator of goodness". The meaning of this "intermediary of goodness" is obtained through the formulation of the syntagmatic field, namely to comprehend a series of semantically interconnected words such as "double and rescue" meaning. Semantically terminology, the meaning of the word hadith is "Whosoever intercedes for a good to his brother, then he is rewarded and received, then he has come to a great door of the doors of usury. 2). Hadith about Islamic economic ethics narrated by Muslims. The word focus of the hadith is the word "gharar" which is semantically interpreted by "deception". The meaning of "deception" is obtained through the formulation of the syntagmatic field, namely by understanding the meaning of words that are still relevant to the word "deception", such as the word "unclear, and syubhat". In semantic terms, the meaning of the hadith is "Rasulullah Saw forbade buying by throwing stones and buying fraud. 3). Hadiths about Islamic economic ethics that Bukhori maintains. The focus word of the hadith is "Najsyi" which is semantically interpreted as "deceiving". The meaning of "deceiving" is derived from the paradigmatic field formulation, ie by understanding the relationship between elements at a certain level and other language elements such as "unclean, dirty, and disease" meaning. In semantic terms, the meaning of the hadith is "That the Messenger of Allah forbade buying by way of deceiving".

Semantic relation computing theory and its application

Kecskes: Linguistic underspecification of utterance content is widely accepted across different frameworks, including the neo-Gricean approaches (cf. Horn 2005; Levinson 2000) and relevance theory (Carston 2002, 2005; Sperber & Wilson 1986, 1995). There is also an agreement that if linguistic underdeterminacy is given, pragmatic inference is required if a hearer is to recover a speaker's meaning successfully. In your Default Semantics, you reject the idea of underdetermined semantic representation, and offer an alternative approach in which semantic representation is established with the help of intentions in communication. This means that intentions “intrude” into the semantic representation, and the semantic and pragmatic components are interwoven. What are the advantages of this one-level semantics as opposed to the modular view? It can be argued that, in a way, Default Semantics is also a modular approach because intentions can be considered preverbal thoughts generated in the “conceptualizer” and linguistically shaped in the “formulator” using Levelt's terminology (Levelt 1989, 1999). Do you agree with this line of thinking?

Built upon a wealth of neuroimaging, neurostimulation, and neuropsychology data, a recent proposal set forth a framework termed controlled semantic cognition (CSC) to account for how the brain underpins the ability to flexibly use semantic knowledge (Lambon Ralph et al., 2017; Nature Reviews Neuroscience). In CSC, the ‘semantic control’ system, underpinned predominantly by the prefrontal cortex, dynamically monitors and modulates the ‘semantic representation’ system that consists of a ‘hub’ (anterior temporal lobe, ATL) and multiple ‘spokes’ (modality-specific areas). CSC predicts that unfamiliar and exacting semantic tasks should intensify communication between the ‘control’ and ‘representation’ systems, relative to familiar and less taxing tasks. In the present study, we used functional magnetic resonance imaging (fMRI) to test this hypothesis. Participants paired unrelated concepts by canonical colours (a less accustomed task – e.g., pairing ketchup with fire-extinguishers due to both being red) or paired well-related concepts by semantic relationship (a typical task – e.g., ketchup is related to mustard). We found the ‘control’ system was more engaged by atypical than typical pairing. While both tasks activated the ATL ‘hub’, colour pairing additionally involved occipitotemporal ‘spoke’ regions abutting areas of hue perception. Furthermore, we uncovered a gradient along the ventral temporal cortex, transitioning from the caudal ‘spoke’ zones preferring canonical colour processing to the rostral ‘hub’ zones preferring semantic relationship. Functional connectivity also differed between the tasks: Compared with semantic pairing, colour pairing relied more upon the inferior frontal gyrus, a key node of the control system, driving enhanced connectivity with occipitotemporal ‘spoke’. Together, our findings characterise the interaction within the neural architecture of semantic cognition – the control system dynamically heightens its connectivity with relevant components of the representation system, in response to different semantic contents and difficulty levels.

It is debated whether cortical responses matching the time scales of phrases and sentences mediate the mental construction of the syntactic chunks or are simply caused by the semantic properties of words. Here, we investigate to what extent delta-band neural responses to speech can be explained by semantic relatedness between words. To dissociate the contribution of semantic relatedness from sentential structures, participants listened to sentence sequences and paired-word sequences in which semantically related words repeated at 1 Hz. Semantic relatedness in the 2 types of sequences was quantified using a word2vec model that captured the semantic relation between words without considering sentential structure. The word2vec model predicted comparable 1-Hz responses with paired-word sequences and sentence sequences. However, empirical neural activity, recorded using magnetoencephalography, showed a weaker 1-Hz response to paired-word sequences than sentence sequences in a word-level task that did not require sentential processing. Furthermore, when listeners applied a task-related rule to parse paired-word sequences into multi-word chunks, 1-Hz response was stronger than that in word-level task on the same sequences. Our results suggest that cortical activity tracks multi-word chunks constructed by either syntactic rules or task-related rules, whereas the semantic relatedness between words contributes only in a minor way.

The meanings of words are not permanent but change over time. Some changes of meaning are quick, such as when a pronoun changes its reference; some are slower, as when two speakers find out that they are using the same word in different senses; and some are very slow, such as when the meaning of a word changes over historical time. A theory of semantics should account for these different time scales. In order to describe these different types of meaning changes, I present an analysis of three levels of communication: instruction, coordination of common ground and coordination of meaning. My first aim is to show that these levels must be considered when discussing lexical semantics. A second aim is to use the levels to identify the communicative roles of some of the main word classes, in particular nouns, adjectives, verbs, indexicals and quantifiers. I argue that the existence of word classes can, to a large extent, be explained by the communicative needs that arise on the different levels.

- The aim of this paper is to analyse the analogy of the lexicon with a space defined by words, which is common to a number of computational models of meaning in cognitive science. This can be regarded as a case of constitutive scientific metaphor in the sense of Boyd (1979) and is grounded in the so-called Distributional Hypothesis, stating that the semantic similarity between two words is a function of the similarity of the linguistic contexts in which they typically co-occur. The meaning of words is represented in terms of their topological relations in a high-dimensional space, defined by their combinatorial behaviour in texts. A key consequence of adopting the metaphor of word spaces is that semantic representations are modelled as highly context-sensitive entities. Moreover, word space models promise to open interesting perspectives for the study of metaphorical uses in language, as well as of lexical dynamics in general. Keywords: Cognitive sciences, Computational linguistics, Distributional models of the lexicon, Metaphor, Semantics, Word spaces.

How accentuation influences semantic short-term memory representations during on-line speech processing: an event-related potential study

Relational semantics for full linear logic