Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?

The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, On-line Support Groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically verify the presence of therapeutic factors in social networking websites by using Natural Language Processing (NLP) techniques. The methodology is evaluated on On-line asynchronous multi-party conversations collected from an OSG and Twitter. The results of the analysis indicate that therapeutic factors occur more frequently in OSG conversations than in Twitter conversations. Moreover, the analysis of OSG conversations reveals that the users of that platform are supportive, and interactions are likely to lead to the improvement of their emotional state. We believe that our method provides a stepping stone towards automatic analysis of emotional states of users of online platforms. Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users' mental health, and/or support in the analysis of their impact on specific individuals

Concept Tagging for Natural Language Understanding:Two Decadelong Algorithm Development

Concept tagging is a type of structured learning needed for natural language understanding (NLU) systems. In this task, meaning labels from a domain ontology are assigned to word sequences. In this paper, we review the algorithms developed over the last twenty five years. We perform a comparative evaluation of generative, discriminative and deep learning methods on two public datasets. We report on the statistical variability performance measurements. The third contribution is the release of a repository of the algorithms, datasets and recipes for NLU evaluation.L’annotazione automatica dei concetti è un tipo di apprendimento strutturato necessario per i sistemi di comprensione del linguaggio natural (NLU). In questo processo le etichette di un’ontologia di dominio sono assegnate a sequenze di parole. In questo articolo esaminiamo gli algoritmi sviluppati negli ultimi venticinque anni. Eseguiamo una valutazione comparativa dei metodi di apprendimento generativo, discriminatorio e approfondito su due set di dati pubblici. Il secondo contributo è un’analisi della variabilitá delle misure di valutazione. Il terzo contributo è il rilascio di un archivio degli algoritmi, dei sets di dati e delle ricette per la valutazione dell’NLU

Dual fermion method as a prototype of generic reference-system approach for correlated fermions

We present a purely diagrammatic derivation of the dual fermion scheme [Phys. Rev. B 77 (2008) 033101]. The derivation makes particularly clear that a similar scheme can be developed for an arbitrary reference system provided it has the same interaction term as the original system. Thereby no restrictions are imposed by the locality of the reference problem or by the nature of the original problem as a lattice one. We present new arguments in favour of keeping the dual denominator in the expression for the lattice self-energy independently of the truncation of the dual interaction. As an example we present the computational results for the half-filled 2D Hubbard model with the choice of a $2\times2$ plaquette with periodic boundary conditions as a reference system. We observe that obtained results are in a good agreement with numerically exact lattice quantum Monte Carlo data

Irony Detection: from the Twittersphere to the News Space

Automatic detection of irony is one of the hot topics for sentiment analysis, as it changes the polarity of text. Most of the work has been focused on the detection of figurative language in Twitter data due to relative ease of obtaining annotated data, thanks to the use of hashtags to signal irony. However, irony is present generally in natural language conversations and in particular in online public fora. In this paper, we present a comparative evaluation of irony detection from Italian news fora and Twitter posts. Since irony is not a very frequent phenomenon, its automatic detection suffers from data imbalance and feature sparseness problems. We experiment with different representations of text – bag-of-words, writing style, and word embeddings to address the feature sparseness; and balancing techniques to address the data imbalanc

Automatically Predicting User Ratings for Conversational Systems

Automatic evaluation models for open-domain conversational agents either correlate poorly with human judgment or require expensive annotations on top of conversation scores. In this work we investigate the feasibility of learning evaluation models without relying on any further annotations besides conversation-level human ratings. We use a dataset of rated (1-5) open domain spoken conversations between the conversational agent Roving Mind (competing in the Amazon Alexa Prize Challenge 2017) and Amazon Alexa users. First, we assess the complexity of the task by asking two experts to re-annotate a sample of the dataset and observe that the subjectivity of user ratings yields a low upper-bound. Second, through an analysis of the entire dataset we show that automatically extracted features such as user sentiment, Dialogue Acts and conversation length have significant, but low correlation with user ratings. Finally, we report the results of our experiments exploring different combinations of these features to train automatic dialogue evaluation models. Our work suggests that predicting subjective user ratings in open domain conversations is a challenging task.I modelli stato dell’arte per la valutazione automatica di agenti conversazionali open-domain hanno una scarsa correlazione con il giudizio umano oppure richiedono costose annotazioni oltre al punteggio dato alla conversazione. In questo lavoro investighiamo la possibilità di apprendere modelli di valutazione attraverso il solo utilizzo di punteggi umani dati all’intera conversazione. Il corpus utilizzato è composto da conversazioni parlate open-domain tra l’agente conversazionale Roving Mind (parte della competizione Amazon Alexa Prize 2017) e utenti di Amazon Alexa valutate con punteggi da 1 a 5. In primo luogo, valutiamo la complessità del task assegnando a due esperti il compito di riannotare una parte del corpus e osserviamo come esso risulti complesso perfino per annotatori umani data la sua soggettività. In secondo luogo, tramite un’analisi condotta sull’intero corpus mostriamo come features estratte automaticamente (sentimento dell’utente, Dialogue Acts e lunghezza della conversazione) hanno bassa, ma significativa correlazione con il giudizio degli utenti. Infine, riportiamo i risultati di esperimenti volti a esplorare diverse combinazioni di queste features per addestrare modelli di valutazione automatica del dialogo. Questo lavoro mostra la difficoltà del predire i giudizi soggettivi degli utenti in conversazioni senza un task specifico

Iron metallurgy of the Xianbei period in Tuva (Southern Siberia)

We present results of the complex investigation of large-scale iron production at the site of Katylyg 5 (Tuva, Southern Siberia) dating to 3rd-4th c. AD. The excavations have uncovered nine trapezoid underground smelting furnaces, a tonne of smelting slag, smithing remains and a charcoal production zone. The investigation of slag by Optical microscopy, SEM-EDS and ICP-MS confirms the performance of smelting and smithing operations at the site, and also suggests that the smelted ore was magnetite, associated with quartz. The presence of copper (bronze) prills in most of the smithing slag indicates that copper was worked alongside iron in the smithing hearths. The spatial division of the site into three different production zones (smelting, smithing and charcoalproduction) suggests a well-organized and self-sufficient industry, that was probably tightly controlled throughout all stages of the chaîne op´eratoire. The trapezoid furnaces identified at Katylyg, are also known from Cis-Baikal region where they date from the end of the 1st millennium BCE and throughout most of the 1st millennium AD. This suggests that the technology of trapezoid furnaces, along with the Kokel culture to which they are attributed, likely emerged in Tuva with the migrations from the Baikal region due to the westward Xianbei expansion during 1st-3rd c. AD