1 tudományos segédmunkatárs, mTa Társadalomtudományi Kutatóközpont "lendület" rECENS Kutatócsoport 2 tudományos segédmunkatárs, mTa Társadalomtudományi Kutatóközpont "lendület" rECENS Kutatócsoport 3 kutató, mTa Társadalomtudományi Kutatóközpont "lendület" rECENS Kutatócsoport 4 tudományos segédmunkatárs, mTa Társadalomtudományi Kutatóközpont "lendület" rECENS Kutatócsoport, egyetemi tanársegéd, Szegedi Tudományegyetem Bölcsészettudományi Kar orosz Filológiai Tanszék 5 PhD, az mTa Társadalomtudományi Kutatóközpont "lendület" rECENS Kutatócsoport vezetője

The analysis of social discourses from the perspective of historical changes deserves special attention. Such a study could play a key role in revealing social changes and latent narrative of those in power; and understanding the underlying social dynamic in a given period. Until the recent years, such issues were analyzed mainly in a qualitative approach. In our paper we present a new way of revealing/discovering and interpreting social discourses using an advanced NLP method called word embedding. Based on word similarities we can understand the main structural frames of a given system and using a dynamic approach we can reveal the social changes in a historical period. In our study we created a large corpus from the Hungarian "P art elet" journal . This was the official journal of the governing party, hence it represents not just a media discourse of the era, but the official discourse of the government, too. One of the main focal points of our research is to study the evolution of the semantic content of some of the concepts related to the topics of agriculture and industry, which are two central notions of the examined era.

No abstract

In this paper, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer’s Disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients and mAD patients, we will carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the paper, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we will analyze the efficacy of each feature group separately. Our model which uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68–70%, 2-class accuracy values: 77.3–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.