66 citations · 104 across the 17 of their papers we have counts for
21 papers
MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)
Paolo Pedinotti, Peter Baumann, Nathan Jessurun +2
Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge graphs from scientific corpora…
Generalizing over Long Tail Concepts for Medical Term Normalization
Beatrice Portelli, Simone Scaboro, Enrico Santus +3
Medical term normalization consists in mapping a piece of text to a large number of output classes. Given the small size of the annotated datasets and the extremely long tail distr…
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni +2
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task. We explored the limits of Transformer based models on text classification, entity ex…
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni +2
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of re…
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
Beatrice Portelli, Daniele Passabì, Edoardo Lenzi +3
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeki…
A Structured Distributional Model of Sentence Meaning and Processing
Emmanuele Chersoni, Enrico Santus, Ludovica Pannitto +3
Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines w…
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Zhijing Jin, Di Jin, Jonas Mueller +2
Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This t…
GraphIE: A Graph-Based Framework for Information Extraction
Yujie Qian, Enrico Santus, Zhijing Jin +2
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuabl…
A Rank-Based Similarity Metric for Word Embeddings
Enrico Santus, Hongmin Wang, Emmanuele Chersoni +1
Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation be…
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes
Enrico Santus, Chris Biemann, Emmanuele Chersoni
This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies…
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Emmanuele Chersoni, Enrico Santus, Philippe Blache +1
Despite the number of NLP studies dedicated to thematic fit estimation, little attention has been paid to the related task of composing and updating verb argument expectations. The…
Measuring Thematic Fit with Distributional Feature Overlap
Enrico Santus, Emmanuele Chersoni, Alessandro Lenci +1
In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of…
German in Flux: Detecting Metaphoric Change via Word Entropy
Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus +2
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We also build…
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
Vered Shwartz, Enrico Santus, Dominik Schlechtweg
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. W…
CogALex-V Shared Task: ROOT18
Emmanuele Chersoni, Giulia Rambelli, Enrico Santus
In this paper, we describe ROOT 18, a classifier using the scores of several unsupervised distributional measures as features to discriminate between semantically related and unrel…
Testing APSyn against Vector Cosine on Similarity Estimation
Enrico Santus, Emmanuele Chersoni, Alessandro Lenci +2
In Distributional Semantic Models (DSMs), Vector Cosine is widely used to estimate similarity between word vectors, although this measure was noticed to suffer from several shortco…
Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity
Emmanuele Chersoni, Enrico Santus, Alessandro Lenci +2
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separ…
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can…
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 unsu…
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
Enrico Santus, Alessandro Lenci, Tin-Shing Chiu +2
ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies o…
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, ca…