A context based model for sentiment analysis in twitter for the italian language

Studi recenti per la Sentiment Analysis in Twitter hanno tentato di creare modelli per caratterizzare la polarit´a di un tweet osservando ciascun messaggio in isolamento. In realt`a, i tweet fanno parte di conversazioni, la cui natura pu`o essere sfruttata per migliorare la qualit`a dell’analisi da parte di sistemi automatici. In (Vanzo et al., 2014) `e stato proposto un modello basato sulla classificazione di sequenze per la caratterizzazione della polarit` a dei tweet, che sfrutta il contesto in cui il messaggio `e immerso. In questo lavoro, si vuole verificare l’applicabilit`a di tale metodologia anche per la lingua Italiana.Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribution of this information has been recently investigated for the English language by modeling the polarity detection as a sequential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian

GAN-BERT: Generative adversarial learning for robust text classification with a bunch of labeled examples

Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In many real scenarios, obtaining high- quality annotated data is expensive and time consuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected. One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi- Supervised Generative Adversarial Networks. In this paper, we propose GAN-BERT that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks

Follow-on Question Suggestion via Voice Hints for Voice Assistants

The adoption of voice assistants like Alexa or Siri has grown rapidly, allowing users to instantly access information via voice search. Query suggestion is a standard feature of screen-based search experiences, allowing users to explore additional topics. However, this is not trivial to implement in voice-based settings. To enable this, we tackle the novel task of suggesting questions with compact and natural voice hints to allow users to ask follow-up questions. We define the task, ground it in syntactic theory and outline linguistic desiderata for spoken hints. We propose baselines and an approach using sequence-to-sequence Transformers to generate spoken hints from a list of questions. Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation. Results show that a naive approach of concatenating suggested questions creates poor voice hints. Our approach, which applies a linguistically-motivated pretraining task was strongly preferred by humans for producing the most natural hints.Comment: Accepted as Long Paper at EMNLP'23 Finding

Context-aware Models for Twitter Sentiment Analysis

Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated for the detection of the polarity of tweet messages. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model has been here adopted to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging better embodies evidence about the contexts and is able to increase the accuracy of the resulting polarity detection process. These evidences are strengthened as experiments are successfully carried out over two different languages: Italian and English. Results are particularly interesting as the approach is flexible and does not rely on any manually coded resources

Learning to Solve NLP Tasks in an Incremental Number of Languages

In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of target languages grows. Moreover, the original annotated material may no longer be available due to storage or business constraints. Re-training only with the new language data will inevitably result in Catastrophic Forgetting of previously acquired knowledge. We propose a Continual Learning strategy that updates a model to support new languages over time, while maintaining consistent results on previously learned languages. We define a Teacher-Student framework where the existing model "teaches" to a student model its knowledge about the languages it supports, while the student is also trained on a new language. We report an experimental evaluation in several tasks including Sentence Classification, Relational Learning and Sequence Labeling

Neural sentiment analysis for a real-world application

In this paper, we describe our neural network models for a commercial application on sentiment analysis. Different from academic work, which is oriented towards complex networks for achieving a marginal improvement, real scenarios require flexible and efficient neural models. The possibility to use the same models on different domains and languages plays an important role in the selection of the most appropriate architecture. We found that a small modification of the state-of-the-art network according to academic benchmarks led to a flexible neural model that also preserves high accuracy. In questo lavoro, descriviamo i nostri modelli di reti neurali per un'applicazione commerciale basata sul sentiment analysis. A differenza del mondo accademico, dove la ricerca è orientata verso reti anche complesse per il raggiungimento di un miglioramento marginale, gli scenari di utilizzo reali richiedono modelli neurali flessibili, efficienti e semplici. La possibilitá di utilizzare gli stessi modelli per domini e linguaggi variegati svolge un ruolo importante nella scelta dell'architettura. Abbiamo scoperto che una piccola modifica della rete allo stato dell'arte rispetto ai benchmarks accademici produce un modello neurale flessibile che preserva anche un'elevata precisione

CARE-compliant stereotactic radiotherapy of urothelial nodal metastases: A case report

The aim of the present study was to report the case of a 58-year-old male patient with ureteral carcinoma who underwent ureteroileostomy treatment. At 2 years following surgery, six lymph node metastases (LNMs) were detected in the patient's para-aortic and pelvic regions using F-18-labeled fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/CT. All LNMs were treated using stereotactic body radiotherapy (SBRT; 35-40 Gy/5 fractions). At 3 months after radiotherapy, F-18-FDG-PET/CT examination revealed a complete radiological and metabolic response of all targeted treatment sites in the patient. In the 2 years following radiotherapy, another three same-dose SBRT treatments were performed on single or multiple LNMs, which were all detected in the abdomen and pelvis of the patient. Overall, a total of 11 LNMs were targeted in the patient and all exhibited complete radiological and metabolic response following treatment. The only treatment side effect reported by the patient was a slight and temporary loss of appetite. In patients with lymph node oligometastases there are two options for radiotherapy: i) Irradiation focusing on LNMs alone; and ii) prophylactic irradiation of the entire lymph node area combined with a boost on macroscopic lesions. In the patient discussed in the present study, the choice of irradiation focusing on LNMs alone made it possible to postpone systemic therapies and instead use an optimally tolerated treatment. The treatment outcome in this patient indicated that there was no radioresistance of urothelial LNMs

Complete metabolic response after Partially Ablative Radiotherapy (PAR) for bulky retroperitoneal liposarcoma: A case report

: In the management of symptomatic inoperable retroperitoneal sarcomas (RPS), palliative radiotherapy (RT) is a potential treatment option. However, the efficacy of low doses used in palliative RT is limited in these radioresistant tumors. Therefore, exploring dose escalation strategies targeting specific regions of the tumor may enhance the therapeutic effect of RT in relieving or preventing symptoms. In this case report, we present the case of an 87-year-old patient with rapidly growing undifferentiated liposarcoma in the retroperitoneum, where surgical and systemic therapies were ruled out due to age and comorbidities. RT was administered using volumetric modulated arc therapy, delivering 20 Gy in 4 fractions twice daily to the macroscopic tumor and 40 Gy in 4 fractions twice daily (simultaneous integrated boost) to the central part of the tumor (Gross Tumor Volume minus 2 cm). An 18F-FDG-PET-CT scan performed after RT demonstrated a complete metabolic response throughout the entire tumor mass. Although the patient eventually succumbed to metastatic spread to the bone, liver, and lung after 9 months, no local disease progression or pain/obstructive symptoms were observed. This case highlights the technical and clinical feasibility of delivering ablative doses of RT to the central region of the tumor and suggests the potential for achieving a complete metabolic response and durable tumor control