The syntax of metaphor

2021 ◽  
Vol 9 (1) ◽  
pp. 47-70
Author(s):  
Ulrike Schneider
Keyword(s):  
Random Forest ◽  
Metaphorical Extension ◽  
Diachronic Changes ◽  
Semantic Properties

Abstract This paper analyses diachronic changes which result from metaphorical extension. Its aim is to assess whether such semantic shifts may lead to further semantic and syntactic differentiation between the verb senses and whether they can be described as shifts away or towards prototypical transitivity (cf. Hopper & Thompson 1980). It focusses on changes the verb derail underwent in the 19th and 20th centuries. In a corpus-based analysis, it utilises CART trees and a random forest to determine which syntactic and semantic properties differentiate literal and metaphorical uses of derail. Results reveal a syntactic shift from transitive to intransitive in the older literal construction which hardly affects the younger metaphorical one. This indicates that differentiation can be an epiphenomenon of semantic shifts.

Evaluation of Semantic Properties of Equivalent Abstract Pictures

2009 ◽  
Author(s):  
Renato Bortoloti ◽  
Julio C. De Rose
Keyword(s):  
Semantic Properties

Semantic Properties Questionnaire

2002 ◽  
Author(s):  
Stanley B. Klein ◽  
Leda Cosmides ◽  
Kristi A. Costabile ◽  
Lisa Mei
Keyword(s):  
Semantic Properties

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Nerdalicious scientainment: A network analysis of English libfixes

2019 ◽  
Vol 12 (3) ◽  
pp. 353-384 ◽  
Author(s):  
Muriel Norde ◽  
Sarah Sippach
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Phonological Similarity ◽  
High Type ◽  
Type Frequency ◽  
Semantic Properties

Libfixes are parts of words that share properties with both blends, compounds and affixes. They are deliberate formations, often with a jocular character, e.g. nerdalicious ‘delicious for nerds’, or scientainment ‘scientific entertainment’. These are not one-off formations – some libfixes have become very productive, as evidenced by high type frequency in a single corpus. Libfix constructions are particularly interesting for a network analysis for three reasons: they do not always have discrete morpheme boundaries, they feature a wide variety of bases (including phrases, as in give-me-a-break-o-meter), and they may be the source of back formations such as infotain. In this paper, we present a corpus-based analysis of eight English libfixes (cracy, fection, flation, gasm, licious, (o-)meter, tainment, and tastic), detailing their formal and semantic properties, as well as their differences and similarities. We argue that libfixes are most fruitfully analysed in a Bybeean network model, in which nodes are connected on the basis of phonological similarity, which allows for both fully compositional and non-compositional constructions to be linked without an exhaustive analysis into morphemes.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio

Random Forest: A Review

2017 ◽  
Vol 7 (1) ◽  
pp. 251-257 ◽  
Author(s):  
Eesha Goel ◽  
◽  
Er. Abhilasha ◽  
Keyword(s):  
Random Forest
Sign in / Sign up

Export Citation Format

Share Document