Explore the vast range of titles available.

Technological recombinative innovation is a crucial way of innovation, and detecting technological recombination can effectively identify the technical elements with recombinative innovation potential in the future. This study proposes a novel method for detecting technological recombination by combining semantic analysis and dynamic network analysis. The framework accurately captures the hidden semantic changes behind keywords over time and deeply excavates the dynamic evolution characteristics of keyword networks in the development process, which effectively improves the accuracy of the identification results of technological recombination. Firstly, the dynamic word embedding model is applied to generate word vectors, and construct the dynamic keyword networks. Then, the dynamic network link prediction method is trained to predict the future network and the possibility of connection between keywords is calculated, which represents the technological recombination potential value. Finally, in order to identify potential recombination opportunities of crucial technologies in the field, SLM community detection is combined with the PageRank algorithm to identify core keywords in communities of the future network, and then technological recombination candidates corresponding to core keywords are detected. A case study on artificial intelligence domain demonstrates the reliability of the methodology.

Expressivists about epistemic modals deny that ‘Jane might be late’ canonically serves to express the speaker’s acceptance of a certain propositional content. Instead, they hold that it expresses a lack of acceptance (that Jane isn’t late). Prominent expressivists embrace pragmatic expressivism: the doxastic property expressed by a declarative is not helpfully identified with (any part of) that sentence’s compositional semantic value. Against this, we defend semantic expressivism about epistemic modals: the semantic value of a declarative from this domain is (partly) the property of doxastic attitudes it canonically serves to express. In support, we synthesize data from the critical literature on expressivism—largely reflecting interactions between modals and disjunctions—and present a semantic expressivism that readily predicts the data. This contrasts with salient competitors, including: pragmatic expressivism based on domain semantics or dynamic semantics; semantic expressivism à la Moss (Semant Pragmat 8(5):1–81, 2015. https://doi.org/10.3765/sp. 8.5); and the bounded relational semantics of Mandelkern (Philos Rev 128(1):1–61, 2019. https://doi.org/10.1215/00318108-7213001).

This article provides an account of the distribution and interpretation of POLARITY PARTICLES in responses, starting with yes and no in English, and then extending the coverage to their crosslinguistic kin. Polarity particles are used in responses to both declarative and interrogative sentences, and thus provide a window onto the semantics and discourse effects of such sentences. We argue that understanding the distribution and interpretation of polarity particles requires a characterization of declaratives and interrogatives that captures a series of challenging similarities and differences across these two sentence types. To meet this challenge we combine and extend insights from inquisitive semantics, dynamic semantics, and commitment-based models of discourse. We then provide a full account of the English data that leads to a typology of polarity particles and a series of crosslinguistic predictions. These predictions are checked against data from Romanian, Hungarian, French, and German, languages that contrast with English in that they have ternary polarity particle systems, and contrast with one another in further subtle ways.

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far the systems that LFIT handled were mainly restricted to synchronous deterministic dynamics. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we propose a modeling of discrete memory-less multi-valued dynamic systems as logic programs in which a rule represents what can occur rather than what will occur. This modeling allows us to represent non-determinism and to propose an extension of LFIT to learn regardless of the update schemes, allowing to capture a large range of semantics. We also propose a second algorithm which is able to learn a whole system dynamics, including its semantics, in the form of a single propositional logic program with constraints. We show through theoretical results the correctness of our approaches. Practical evaluation is performed on benchmarks from biological literature.

Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

It is known that a multiple- wh question can inquire about a dependency between wh -expressions, termed “ wh -dependency”, admitting a pair-list answer. In discourse, the dependency can even support a “plural” interpretation of a singular pronoun. Consider Which u girl bought which ν book and how much did s h e u pay for it ν ? The multiple- wh question is asking about a dependency between girls and books. In the subsequent question, the dependency is referred to and the resolution of the singular pronouns varies among the girls and the books they bought. While van Rooy (In: Hulstijn and Nijholt (eds.) Modal subordination in questions, Twendial, 1998) and Dotlačil & Roelofsen (Dynamic inquisitive semantics, Amsterdam, 2019) have noticed this phenomenon, it is still understudied. Based on their insights, this paper argues that reference to wh -dependencies is facilitated in two types of subordination constructions: quantificational subordination, where reference to wh -dependencies is facilitated through quantificational structure, and question subordination, where reference to wh -dependencies is facilitated through conjoining questions. Building on Hamblin-Karttunen semantics of questions and Dynamic Plural Logic (DPlL), this paper proposes a dynamic family-of-questions approach to multiple- wh questions. In particular, a multiple- wh question generates a set of sub-questions, i.e., a family of questions, and then the set is transformed into a set of possible pair-list answers. Both encode the relevant wh -dependency in a different manner. As a consequence, the dependency encoded in a family of questions is retrievable in question subordination, while the one encoded in a pair-list answer is retrievable in quantificational subordination.

This paper develops a way to model performative speech acts within a framework of dynamic semantics. It introduces a distinction between performative and informative updates, where informative updates filter out indices of context sets (cf. Stalnaker, Cole (ed), Pragmatics, Academic Press, 1978), whereas performative updates change their indices (cf. Szabolcsi, Kiefer (ed), Hungarian linguistics, John Benjamins, 1982). The notion of index change is investigated in detail, identifying implementations by a function or by a relation. Declarations like the meeting is (hereby) adjourned are purely performative updates that just enforce an index change on a context set. Assertions like the meeting is (already) adjourned are analyzed as combinations of a performative update that introduces a guarantee of the speaker for the truth of the proposition, and an informative update that restricts the context set so that this proposition is true. The first update is the illocutionary act characteristic for assertions; the second is the primary perlocutionary act, and is up for negotiations with the addressee. Several other speech acts will be discussed, in particular commissives, directives, exclamatives, optatives, and definitions, which are all performative, and differ from related assertions. The paper concludes a discussion of locutionary acts, which are modelled as index changers as well, and proposes a novel analysis for the performative marker hereby.

Reading, naming, and repetition are classical neuropsychological tasks widely used in the clinic and psycholinguistic research. While reading and repetition can be accomplished by following a direct or an indirect route, pictures can be named only by means of semantic mediation. By means of fMRI multivariate pattern analysis, we evaluated whether this well‐established fundamental difference at the cognitive level is associated at the brain level with a difference in the degree to which semantic representations are activated during these tasks. Semantic similarity between words was estimated based on a word association model. Twenty subjects participated in an event‐related fMRI study where the three tasks were presented in pseudo‐random order. Linear discriminant analysis of fMRI patterns identified a set of regions that allow to discriminate between words at a high level of word‐specificity across tasks. Representational similarity analysis was used to determine whether semantic similarity was represented in these regions and whether this depended on the task performed. The similarity between neural patterns of the left Brodmann area 45 (BA45) and of the superior portion of the left supramarginal gyrus correlated with the similarity in meaning between entities during picture naming. In both regions, no significant effects were seen for repetition or reading. The semantic similarity effect during picture naming was significantly larger than the similarity effect during the two other tasks. In contrast, several regions including left anterior superior temporal gyrus and left ventral BA44/frontal operculum, among others, coded for semantic similarity in a task‐independent manner. These findings provide new evidence for the dynamic, task‐dependent nature of semantic representations in the left BA45 and a more task‐independent nature of the representational activation in the lateral temporal cortex and ventral BA44/frontal operculum. The current multivariate pattern analysis (MVPA) study revealed a neurobiological basis for the distinction between task‐dependent and task‐independent retrieval of word meaning. Whereas the left BA45 and supramarginal gyrus showed a semantic similarity effect during picture naming only, the anterior STG and ventral BA44/frontal operculum, among other regions, showed a task‐independent semantic similarity effect.

The dynamic categorization theory of word meaning is a novel trend of the development of categorization theory. Loss or weakening of the meaning, generalization or being empty, lexicalization or grammaticalization are all relevant to the dynamic nature of semantic categories. This paper explores the motivations behind the dynamic categorization of word meanings. We summarize objective motivations based on previous research and linguistic examples in Chinese and English, and investigate subjective motivations through empirical research involving questionnaires and Python visualization tools. Findings reveal that: (1) The dynamic nature of categorical attributes and the gradience of categories are the objective motivations of dynamic categorization of word meanings, while dynamic construal is the subjective motivation. (2) The variability of category members and the vagueness and openness of category boundaries enable diachronic evolution, and the gradience of category determines the gradual change of categories. (3) Dynamic construal in cognitive process allows for the instant construction of semantic categories.

Utterances of natural language sentences can be used to communicate not just contents, but also forces. This paper examines this topic from a cross-linguistic perspective on sentential mood (declarative, interrogative, imperative). Recent work in this area focuses on conversational dynamics: the three sentence types can be associated with distinctive kinds of conversational effects called sentential forces, modeled as three kinds of updates to the discourse context. This paper has two main goals. First, it provides two arguments, on empirical and methodological grounds, for treating sentential force (context update rules) as part of a compositional dynamic semantics, rather than a dynamic pragmatics. Second, it formulates a minimal dynamic semantic analysis that covers the data at the heart of these arguments, incorporating existing analyses of the three major moods, evidentials and conjunction. A further aim of the paper is to sharpen the distinction between sentential force and utterance (illocutionary) force, and discuss its implications.