translation is an active process in which a meaning is transferred from a source to a target language. during this process, meaning must have accordance with two main semantic sub-systems: signification and evaluation. signification is related to a signified unit of a linguistic sign and its encompassing properties known as seme. however, evaluation includes a range of different actualizations ...

The aim of the current study is to propose a system, which can automatically deduce entailment relations of textual pairs. The system mainly uses seven features and a decision tree is utilized as a prediction model of the system and seven features of textual pairs are employed to be input of the prediction model. The experimental results for dataset Formal-run based on our proposed method are e...

The article is devoted to the analysis of semantics and features functioning textual bond "bolee togo" [moreover]. aim work was identify specifics this bond, taking into account its semantic in texts various functional styles genres. Moreover, following research methods have been applied comprehend bond: traditional descriptive method, including observation, generalization systematization lingu...

This paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Althoug...

The textual entailment system determines whether one sentence can entail another in a common sense. We proposed several approaches to train textual entailment classifiers, including setting ancestor distance threshold, expanding training corpus, using different sets of features, and tuning classifier settings. The results show that a MC classifier trained by using an expanded training corpus an...

Mobile SMS spam is on the rise and is a prevalent problem. While recent work has shown that simple machine learning techniques can distinguish between ham and spam with high accuracy, this paper explores the individual contributions of various textual features in the classification process. Our results reveal the surprising finding that simple is better: using the largest spam corpus of which w...

In gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among t...

In the work here presented, we apply textual and sequential methods to assess the outcomes of an unconstrained multiparty dialogue. In the context of chat transcripts from a collaborative learning scenario, we demonstrate that while low-level textual features can indeed predict student success, models derived from sequential discourse act labels are also predictive, both on their own and as a s...

In this paper, we present the LIP6 retrieval system which automatically ranks the most similar images to a given query constituted of both textual and/or visual information through a given textual-visual collection. The system first preprocesses the data set in order to remove stop-words as well as non-informative terms. For each given query, it then finds a ranked list of its most similar imag...