Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based
on 100,000 human semantic proximity judgments. We describe in detail the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible -- diachronic and synchronic -- uses for this dataset.
This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multili
ngual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the potential situations in the use case to FrameNet frames and obtain the relevant lexical units associated to them in the target language, following lexical and semantic criteria, without linguistic expert knowledge. This strategy has been validated and evaluated in two use cases, from industrial scenarios, for interaction in Spanish with a guide robot and with a Computerized Maintenance Management System (CMMS). In both cases, this method has allowed the ontology engineer to instantiate the domain ontology with the intent-relevant information with quality data in a simple and low-resource-consuming manner.
Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore senti
ment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.
We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology
, physics and social sciences. By providing a probabilistic model of graded word meaning we aim to approach the slippery and yet widely used notion of word sense in a novel way. The proposed framework enables us to rigorously compare models of word senses with respect to their fit to the data. We perform extensive experiments and select the empirically most adequate model.
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic regression, random forest, SV
M, and LSTM based models. The second approach involved using a majority voting ensemble of 11 models which were obtained by fine-tuning pre-trained transformer models (BERT, ALBERT, RoBERTa, IndicBERT) after adding an output layer. We found that the second approach was superior for English, Tamil and Malayalam. Our solution got a weighted F1 score of 0.93, 0.75 and 0.49 for English, Malayalam and Tamil respectively. Our solution ranked 1st in English, 8th in Malayalam and 11th in Tamil.
This research aims to study the positives of using the mobile phone in the development of academic education in the faculty of dentistry.
Storage is one of the main services provided by the container terminals to its customers.
The competition between these terminals is very strong to provide the best services and
efficiency that reduces the time of containers in the yards to the low
est time. This reflects
an increase in productivity and reduction in storage costs, that is very important to the
shipping agents, so it had to work on the continuous re-preparation of the yards and
transport equipment commensurate with rapid changes and the great development in the
number of containers.
The study focused on methods of calculating the theoretical storage capacity of the
container terminals and applying them to the research terminal and comparing the results
with the actual reality and actual productivity of the fully and empty container yards, and
the global values of the modern container terminals, and calculation of the values of one of
the most important indicators of use, that is the indicators of use of yard which reflects the
efficiency of the use of available resources in the yards of the station.
This paper explores for Syrian students’ acceptance of e-learning using a
modified UTAUT. An e-learning module was developed from free accessible
web 2.0 technologies to overcome a contingent situation reflected in the loss of
academics in the Syr
ian higher education system.
In this research the best investment for passenger trains on Homs
line - Tartous study through increased tractive mass and speed of
trains using photovoltaic cells and techniques of circuit absorbance
of the locomotive French LDE3200 been an incre
ase in the bloc
running reached by 4895% and speed by 6.25%, and without
modifying the lighting circuits and adapt composite vehicles,
providing the possibility of moving trains day and night.
شThe current study aimed to find the effect of implementing the Science Education Development Program (SEED) on the fourth grade students’ Learning of the Classification Unit. The study followed the quasi-experimental design and used
quantitative and qualitative instruments to analyse the research data.The quantitative instrument was an achievement test, made of (27) items which measure the impact of the program on the fourth graders’ achievement
