In this paper, we describe the results of gender identification from Team DUBL. We used a topic modeling approach for identifying the author’s gender based on his/her written texts. The model was trained on the RusProfiling PAN 2017 Twitter Corpus that contains data in the Russian language. Themodel has been evaluated on texts of other genres, including texts such as letters to a friend, online...

This paper presents example-based machine translation architecture using translation memory that integrates the use of examples for flexible, idiomatic translations with the use of linguistic rules for broad coverage and grammatical accuracy. In examplebased machine translation (EBMT) approach to machine translation is often characterized by its use of a bilingual corpus with parallel texts as ...

This paper presents a method for the syntactic simplification of French texts. Syntactic simplification aims at making texts easier to understand by simplifying complex syntactic structures that hinder reading. Our approach is based on the study of two parallel corpora (encyclopaedia articles and tales). It aims to identify the linguistic phenomena involved in the manual simplification of Frenc...

Various NLP applications require automatic discourse analysis of texts. For analysis of scientific and technical texts, we propose to use all typical lexical units organizing scientific discourse; we call them common scientific words and expressions, most of them are known as discourse markers. The paper discusses features of scientific discourse, as well as the variety of discourse markers spe...

In this paper, a new approach is presented for organizing pedagogical texts for researchers, based on a kernel projection from a hybrid text-researcher space onto the researcher’s ability space, where the text-researcher space includes text’s features as well as the researcher’s reasoning styles and intention in doing research. The approach has been applied to organizing pedagogical texts for t...

Short texts usually encounter data sparsity and ambiguity problems in representations for their lack of context. In this paper, we propose a novel method to model short texts based on semantic clustering and convolutional neural network. Particularly, we first discover semantic cliques in embedding spaces by a fast clustering algorithm. Then, multi-scale semantic units are detected under the su...

In this paper, we study how to generate features from various data representations, such as surface texts and parse trees, for answer extraction. Besides the features generated from the surface texts, we mainly discuss the feature generation in the parse trees. We propose and compare three methods, including feature vector, string kernel and tree kernel, to represent the syntactic features in S...

Traditionally, Controlled Natural Languages (CNLs) are designed either to avoid ambiguity for human readers, or to facilitate automatic semantic analysis, so that texts can be transcoded to a knowledge representation language. CNLs of the second kind have recently been adapted to the requirements of knowledge formation in OWL for the Semantic Web. We suggest in this paper a variant approach bas...