Planning of Electric Taxi Charging Stations Based on Travel Data Characteristics

In view of the practical application requirements for the rapid expansion of electric taxis (ETs) and the reasonable planning of charging stations, this paper presents a method for mining latent semantic correlation of large data by the trajectory of ETs and the planning of charging stations with optimal cost. Firstly, the vector space modeling method of ET trajectory data is studied, and the semantic similarity of the trajectory data matrix is evaluated. Secondly, the hidden characteristics of the mass trajectory data are extracted by matrix decomposition. Then, the latent semantic correlation characteristics of trajectory data are mined. Finally, the fast clustering of ETs is realized by the spectral clustering method. On this basis, with the objective of minimizing the annual construction and maintenance costs of charging stations, the optimal planning scheme of charging stations for ETs is given. In this paper, the spectrum clustering processing method of the potential semantic correlation of the big data of the driving track of ETs can be combined with the operation and maintenance costs of the charging station, and the convenience of charging for ET users is also considered. This provides decision support information for the reasonable planning of charging stations.

Disentangling modal meanings with distributional semantics

This article investigates the collocational behavior of English modal auxiliaries such as may and might with the aim of finding corpus-based measures that distinguish between different modal expressions and that allow insights into why speakers may choose one over another in a given context. The analysis uses token-based semantic vector space modeling (Heylen et al., 2015, Monitoring polysemy. Word space models as a tool for large-scale lexical semantic analysis. Lingua, 157: 153–72; Hilpert and Correia Saavedra, 2017, Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims. Corpus Linguistics and Linguistic Theory) in order to determine whether different modal auxiliaries can be distinguished in terms of their collocational profiles. The analysis further examines whether different senses of the same auxiliary exhibit divergent collocational preferences. The results indicate that near-synonymous pairs of modal expressions, such as may and might or must and have to, differ in their distributional characteristics. Also, different senses of the same modal expression, such as deontic and epistemic uses of may, can be distinguished on the basis of distributional information. We discuss these results against the background of previous empirical findings (Hilpert, 2016, Construction Grammar and its Application to English, 2nd edn. Edinburgh: Edinburgh University Press, Flach, in press, Beyond modal idioms and modal harmony: a corpus-based analysis of gradient idiomaticity in modal-adverb collocations. English Language and Linguistics) and theoretical issues such as degrees of grammaticalization (Correia Saavedra, 2019, Measurements of Grammaticalization: Developing a Quantitative Index for the Study of Grammatical Change. PhD Dissertation, Université de Neuchâtel) and the avoidance of synonymy (Bolinger, 1968, Entailment and the meaning of structures. Glossa, 2(2): 119–27).

A lectometric analysis of aggregated lexical variation in written Standard English with Semantic Vector Space models

Lectometry is a corpus-based methodology that explores how multiple language-external dimensions shape language usage in an aggregate perspective. The paper combines this methodology with Semantic Vector Space modeling to investigate lexical variability in written Standard English, as sampled in the original Brown family of corpora (Brown, LOB, Frown and F-LOB). Based on a joint analysis of 303 lexical variables, which are semi-automatically extracted by means of a SVS, we find that lexical variation in the Brown family is systematically related to three lectal dimensions: discourse type (informative versus imaginative), standard variety (British English versus American English), and time period (1960s versus 1990s). It turns out that most lexical variables are sensitive to at least one of these three language-external dimensions, yet not every dimension has dedicated lexical variables: in particular, distinctive lexical variables for the real time dimension fail to emerge.

Meaning change in a petri dish: constructions, semantic vector spaces, and motion charts

This paper explores how the visualization tool of motion charts can be used for the analysis of meaning change in linguistic constructions. In previous work, linguistic motion charts have been used to represent diachronic frequency trends and changes in the morphosyntactic behavior of linguistic units. The present paper builds on that work, but it shifts the focus to the study of semantic change. How can motion charts be used to visualize semantic change over time? In order to answer this question, we draw on semantic vector space modeling to visualize aspects of linguistic meaning. As an analogy to this approach, the title of this paper alludes to a petri dish in which the growth and development of biological microorganisms can be observed. On the basis of diachronic corpus data, we monitor developments in the semantic ecology of a construction. This allows us to observe processes such as semantic broadening, semantic narrowing, or semantic shift. We illustrate our approach on the basis of a case study that investigates the diachrony of an English construction that we call the ‘many a NOUN’ construction.

Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions

While online conversations are very popular, the content generated by participants is very often overwhelming, poorly organized and often of questionable quality. In this article we use two methods, a text analysis technique, Vector Space Modeling (VSM) and clustering to create a methodology to organize and aggregate information generated by users using Online collaborative Argumentation (OA) in their online debate. An alternative to other widely used conversational tools such as online forums, OA is supposed to help users to join their efforts to construct a shared knowledge representation in the form of an argument map in which multiple points of view can coexist and be presented in the form of a well-organized knowledge object. To see whether this supposition comes into effect we first apply VSM to summarize argument map content as a document space and then use clustering to transform it to a limited number of higher order semantic categories. We apply the methodology to more than 3000 posts created in an online debate of about 160 participants using an online argumentation platform and we show how this methodology can be used to effectively organize and evaluate content generated by a large number of users in online discussions.