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Found 44 papers, 11 papers with code
Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words.
Testing APSyn against Vector Cosine on Similarity Estimation
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 shortcomings.
CogALex-V Shared Task: ROOT18
Our classifier participated in the CogALex-V Shared Task, showing a solid performance on the first subtask, but a poor performance on the second subtask.
Towards a Distributional Model of Semantic Complexity
The composition cost of a sentence depends on the semantic coherence of the event being constructed and on the activation degree of the linguistic constructions.
Measuring Thematic Fit with Distributional Feature Overlap
In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments.
Logical Metonymy in a Distributional Model of Sentence Comprehension
In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e. g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e. g., writing).
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
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.
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes
This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e. g. yellow as discriminative for banana over watermelon).
Ranked #3 on
Relation Extraction
on SemEval 2018 Task 10
A Rank-Based Similarity Metric for Word Embeddings
Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP.
Modeling Violations of Selectional Restrictions with Distributional Semantics
Distributional Semantic Models have been successfully used for modeling selectional preferences in a variety of scenarios, since distributional similarity naturally provides an estimate of the degree to which an argument satisfies the requirement of a given predicate.
A Structured Distributional Model of Sentence Meaning and Processing
In this paper, we propose a Structured Distributional Model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations.
Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning
In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics.
Ciron: a New Benchmark Dataset for Chinese Irony Detection
Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research.
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?
While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models.
Using Conceptual Norms for Metaphor Detection
This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection.
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings
Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one.
Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation
In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e. g.
BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection
Pretrained transformer-based models, such as BERT and its variants, have become a common choice to obtain state-of-the-art performances in NLP tasks.
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums.
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge
Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate.
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context.
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media
Adverse Events (AE) are harmful events resulting from the use of medical products.
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task.
Generalizing over Long Tail Concepts for Medical Term Normalization
Medical term normalization consists in mapping a piece of text to a large number of output classes.
Event knowledge in large language models: the gap between the impossible and the unlikely
Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures
In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have made the attempt successful.
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts.
Comparing Plausibility Estimates in Base and Instruction-Tuned Large Language Models
Experiment 1 shows that, across model architectures and plausibility datasets, (i) log likelihood ($\textit{LL}$) scores are the most reliable indicator of sentence plausibility, with zero-shot prompting yielding inconsistent and typically poor results; (ii) $\textit{LL}$-based performance is still inferior to human performance; (iii) instruction-tuned models have worse $\textit{LL}$-based performance than base models.
Decoding Word Embeddings with Brain-Based Semantic Features
For each probing task, we identify the most relevant semantic features and we show that there is a correlation between the embedding performance and how they encode those features.
Looking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?
Eye-tracking psycholinguistic studies have suggested that context-word semantic coherence and predictability influence language processing during the reading activity.
CMCL 2021 Shared Task on Eye-Tracking Prediction
The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms
In free word association tasks, human subjects are presented with a stimulus word and are then asked to name the first word (the response word) that comes up to their mind.
AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task.
The CogALex Shared Task on Monolingual and Multilingual Identification of Semantic Relations
One containing pairs for each of the training languages (systems were evaluated in a monolingual fashion) and the other proposing a surprise language to test the crosslingual transfer capabilities of the systems.
Is Domain Adaptation Worth Your Investment? Comparing BERT and FinBERT on Financial Tasks
With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures.
CMCL 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior
We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL).
Pragmatic and Logical Inferences in NLI Systems: The Case of Conjunction Buttressing
An intelligent system is expected to perform reasonable inferences, accounting for both the literal meaning of a word and the meanings a word can acquire in different contexts.
Discovering Financial Hypernyms by Prompting Masked Language Models
With the rising popularity of Transformer-based language models, several studies have tried to exploit their masked language modeling capabilities to automatically extract relational linguistic knowledge, although this kind of research has rarely investigated semantic relations in specialized domains.
Lexicon of Changes: Towards the Evaluation of Diachronic Semantic Shift in Chinese
Recent research has brought a wind of using computational approaches to the classic topic of semantic change, aiming to tackle one of the most challenging issues in the evolution of human language.
