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

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

11C-choline vs. 18F-FDG PET/CT in assessing bone involvement in patients with multiple myeloma

<p>Abstract</p> <p>Background</p> <p>Multiple Myeloma (MM) is a B cell neoplasm causing lytic or osteopenic bone abnormalities. Whole body skeletal survey (WBSS), Magnetic resonance (MR) and ¹⁸F-FDG PET/CT are imaging techniques routinely used for the evaluation of bone involvement in MM patients.</p> <p>Aim</p> <p>As MM bone lesions may present low ¹⁸F-FDG uptake; the aim of this study was to assess the possible added value and limitations of ¹¹C-Choline to that of ¹⁸F-FDG PET/CT in patients affected with MM.</p> <p>Methods</p> <p>Ten patients affected with MM underwent a standard ¹¹C-Choline PET/CT and an ¹⁸F-FDG PET/CT within one week. The results of the two scans were compared in terms of number, sites and SUV_maxof lesions.</p> <p>Results</p> <p>Four patients (40%) had a negative concordant ¹¹C-Choline and ¹⁸F-FDG PET/CT scans. Two patients (20%) had a positive ¹¹C-Choline and ¹⁸F-FDG PET/CT scans that identified the same number and sites of bone lesions. The remaining four patients (40%) had a positive ¹¹C-Choline and ¹⁸F-FDG PET/CT scan, but the two exams identified different number of lesions. Choline showed a mean SUV_maxof 5 while FDG showed a mean SUV_maxof 3.8 (P = 0.042). Overall, ¹¹C-Choline PET/CT scans detected 37 bone lesions and ¹⁸F-FDG PET/CT scans detected 22 bone lesions but the difference was not significant (P = 0.8).</p> <p>Conclusion</p> <p>According to these preliminary data, ¹¹C-Choline PET/CT appears to be more sensitive than ¹⁸F-FDG PET/CT for the detection of bony myelomatous lesions. If these data are confirmed in larger series of patients, ¹¹C-Choline may be considered a more appropriate functional imaging in association with MRI for MM bone staging.</p

Dynamic polarity lexicon acquisition for advanced Social Media analytics

Social media analytics tool aims at eliciting information and knowledge about individuals and communities, as this emerges from the dynamics of interpersonal communications in the social networks. Sentiment analysis (SA) is a core component of this process as it focuses onto the subjective levels of this knowledge, including the agreement/rejection, the perception, and the expectations by which individual users socially evolve in the network. Analyzing user sentiments thus corresponds to recognize subjective opinions and preferences in the texts they produce in social contexts, gather collective evidence across one or more communities, and trace some inferences about the underlying social phenomena. Automatic SA is a complex process, often enabled by hand-coded dictionaries, called polarity lexicons , that are intended to capture the a priori emotional aspects of words or multiword expressions. The development of such resources is an expensive, and, mainly, language and task-dependent process. Resulting polarity lexicons may be inadequate at fully covering Social Media phenomena, which are intended to capture global communities. In the area of SA over Social Media, this article presents an unsupervised and language independent method for inducing large-scale polarity lexicons from a specific but representative medium, that is, Twitter. The model is based on a novel use of Distributional Lexical Semantics methodologies as these are applied to Twitter. Given a set of heuristically annotated messages, the proposed methodology transfers the known sentiment information of subjective sentences to individual words. The resulting lexical resource is a large-scale polarity lexicon whose effectiveness is measured with respect to different SA tasks in English, Italian, and Arabic. Comparison of our method with different Distributional Lexical Semantics paradigms confirms the beneficial impact of our method in the design of very accurate SA systems in several natural languages

Injecting sentiment information in context-aware Convolutional Neural Networks

Deep learning models achieved remarkable results in Computer Vision, Speech recognition, Natural Language Processing and Information Retrieval. In this work, we extend a Convolutional Neural Networks (CNNs) for the Sentiment Analysis in Twitter task, as this architecture achieved state-of-the-art results in [3, 4]. In particular, this architecture has been shown effective when a proper pre-training step is adopted to perform the early estimation of the network parameters: in [4] it is suggested to generate pre-training data starting from a randomic selection of Twitter messages annotated with simple heuristics, e.g. the presence of specific emoticons in messages. We improve the quality of such CNN architecture in two ways. First, we propose to adopt a contextual model [5] to select pre-training material from the conversations to which training messages appear, as opposed to an arbitrary selection of messages. In this way, we aim at selecting pre-training messages that could better reflect the topics of the targeted data. Second, we promote the adoption of a multi-channel schema [3] for representing the input data to the CNN. A first channel is used to accommodate lexical information provided by Distributional Models of Lexical Semantics, i.e. a vector representation of words provided by a Word Embedding. The second channel is adopted to represent sentiment oriented information as it is provided by a polarity lexicon. In particular, the sentiment oriented vectors adopted in this study refer to the automatically acquired Distributional Polarity Lexicons, as proposed in [1]. The experimental evaluation shows that the proposed solutions are beneficial w.r.t the targeted task in two languages, i.e. English and Italian. The full version of this paper is provided in [2] and it is available in the SocialNLP@IJCAI 2016 proceedings

Acquiring an Italian polarity lexicon through distributional methods

Recent interests in Sentiment Analysis brought the attention on effective methods to detect opinions and sentiments in texts. Many approaches in literature are based on resources, such as Polarity Lexicons, which model the prior polarity of words or multi-word expressions. Developing such resources is expensive, language dependent, and linguistic sentiment phenomena are not fully covered in them. In this paper an automatic method for deriving polarity lexicons based on Distributional Models of Lexical Semantics is presented. Given a set of heuristically annotated messages from Twitter, we transfer sentiment information from sentences to words. As the approach is mostly unsupervised, it enables the acquisition of polarity lexicons for languages that are lacking these resources.We acquired a polarity lexicon in the Italian language, and experiments on Sentiment Analysis tasks show the benefit of the generated resources