134 research outputs found
Comparing Transformer-based NER approaches for analysing textual medical diagnoses
The automated analysis of medical documents has grown in research interest in recent years as a consequence of the social relevance of the thematic and the difficulties often encountered with short and
very specific documents. In particular, this fervent area of research has stimulated the development of
several techniques of automatic document classification, question answering, and name entity recognition (NER). Nevertheless, many open issues must be addressed to obtain results that are satisfactory for
a field in which the effectiveness of predictions is a fundamental factor in order not to make mistakes
that could compromise people’s lives. To this end, we focused on the name entity recognition task from
medical documents and, in this work, we will discuss the results we obtained by our hybrid approach.
In order to take advantage of the most relevant findings in the field of natural language processing, we
decided to focus on deep neural network models. We compared several configurations of our model by
varying the transformer architecture, such as BERT, RoBERTa and ELECTRA, until we obtained a configuration that we considered the best for our goals. The most promising model was used to participate
in the SpRadIE task of the annual CLEF (Conference and Labs of the Evaluation Forum). The obtained
results are encouraging and can be of reference for future studies on the topic
Analyzing Gaussian distribution of semantic shifts in Lexical Semantic Change Models
In recent years, there has been a significant increase in interest in lexical semantic change
detection. Many are the existing approaches, data used, and evaluation strategies to detect
semantic shifts. The classification of change words against stable words requires thresholds to
label the degree of semantic change. In this work, we compare state-of-the-art computational
historical linguistics approaches to evaluate the efficacy of thresholds based on the Gaussian
Distribution of semantic shifts. We present the results of an in-depth analysis conducted on
both SemEval-2020 Task 1 Subtask 1 and DIACR-Ita tasks. Specifically, we compare Temporal
Random Indexing, Temporal Referencing, Orthogonal Procrustes Alignment, Dynamic Word
Embeddings and Temporal Word Embedding with a Compass. While results obtained with
Gaussian thresholds achieve state-of-the-art performance in English, German, Swedish and
Italian, they remain far from results obtained using the optimal threshold
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images
Scientists in the marine domain process satellite images in order to extract information
that can be used for monitoring, understanding, and forecasting of marine phenomena, such as
turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information
has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence
between the information that can be extracted from the visual data and the interpretation that the
same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting
the quantitative elements of the Earth Observation satellite images with the qualitative information,
modelling this knowledge in a marine phenomena ontology and developing a question answering
mechanism based on natural language that enables the retrieval of the most appropriate data for each
user’s needs. The main objective of the presented methodology is to realize the content-based search
of Earth Observation images related to the marine application domain on an application-specific
basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”
Introducing linked open data in graph-based recommender systems
Thanks to the recent spread of the Linked Open Data (LOD) initiative, a huge amount of machine-readable knowledge encoded as RDF statements is today available in the so-called LOD cloud. Accordingly, a big effort is now spent to investigate to what extent such information can be exploited to develop new knowledge-based services or to improve the effectiveness of knowledge-intensive platforms as Recommender Systems (RS). To this end, in this article we study the impact of the exogenous knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation framework. Specifically, we propose a methodology to automatically feed a graph-based RS with features gathered from the LOD cloud and we analyze the impact of several widespread feature selection techniques in such recommendation settings. The experimental evaluation, performed on three state-of-the-art datasets, provided several outcomes: first, information extracted from the LOD cloud can significantly improve the performance of a graph-based RS. Next, experiments showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize specific evaluation metrics, as accuracy or diversity of the recommendations. Moreover, our graph-based algorithm fed with LOD-based features was able to overcome several baselines, as collaborative filtering and matrix factorization
Automatic selection of linked open data features in graph-based recommender systems
In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically, we investigated whether the integration of LOD-based features can improve the effectiveness of a graph-based recommender system and to what extent the choice of the features selection technique can influence the behavior of the algorithm by endogenously inducing a higher accuracy or a higher diversity. The experimental evaluation showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, our algorithm fed with LODbased features was able to overcome several state-of-the-art baselines: this confirmed the effectiveness of our approach and suggested to further investigate this research line
AlBERTo: Modeling Italian Social Media Language with BERT
Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demonstrating how they can obtain very encouraging results in challenging NLP tasks by generalizing the properties of natural language directly from the data. Models such as ELMo, GPT/GPT-2, BERT, ERNIE, and RoBERTa have proved to be extremely useful in NLP tasks such as entailment, sentiment analysis, and question answering. The availability of these resources mainly in the English language motivated us towards the realization of AlBERTo, a natural language model based on BERT and trained on the Italian language. We decided to train AlBERTo from scratch on social network language, Twitter in particular, because many of the classic tasks of content analysis are oriented to data extracted from the digital sphere of users. The model was distributed to the community through a repository on GitHub and the Transformers library (Wolf et al. 2019) released by the development group huggingface.co. We have evaluated the validity of the model on the classification tasks of sentiment polarity, irony, subjectivity, and hate speech. The specifications of the model, the code developed for training and fine-tuning, and the instructions for using it in a research project are freely available
Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21)
Recommender systems were originally developed as interactive
intelligent systems that can proactively guide users to items that
match their preferences. Despite its origin on the crossroads of HCI
and AI, the majority of research on recommender systems gradually
focused on objective accuracy criteria paying less and less attention
to how users interact with the system as well as the efficacy of
interface designs from users’ perspectives. This trend is reversing
with the increased volume of research that looks beyond algorithms,
into users’ interactions, decision making processes, and overall
experience. The series of workshops on Interfaces and Human
Decision Making for Recommender Systems focuses on the "human
side" of recommender systems. The goal of the research stream
featured at the workshop is to improve users’ overall experience
with recommender systems by integrating different theories of
human decision making into the construction of recommender
systems and exploring better interfaces for recommender systems.
In this summary,we introduce the JointWorkshop on Interfaces and
Human Decision Making for Recommender Systems at RecSys’21,
review its history, and discuss most important topics considered at
the workshop
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