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Found 27 papers, 7 papers with code
black[LSCDiscovery shared task] DeepMistake at LSCDiscovery: Can a Multilingual Word-in-Context Model Replace Human Annotators?
In this paper we describe our solution of the LSCDiscovery shared task on Lexical Semantic Change Discovery (LSCD) in Spanish.
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis
While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance.
black[LSCDiscovery shared task] BOS at LSCDiscovery: Lexical Substitution for Interpretable Lexical Semantic Change Detection
We propose a solution for the LSCDiscovery shared task on Lexical Semantic Change Detection in Spanish.
black[LSCDiscovery shared task] GlossReader at LSCDiscovery: Train to Select a Proper Gloss in English – Discover Lexical Semantic Change in Spanish
In order to conclude if there are any differences between senses of a particular word in two corpora, a human annotator or a system shall analyze many examples containing this word from both corpora.
The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
The repository reflects the task's modularity by allowing model evaluation for WiC, WSI and LSCD.
Enriching Word Usage Graphs with Cluster Definitions
We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions.
A New Massive Multilingual Dataset for High-Performance Language Technologies
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive.
BOS at LSCDiscovery: Lexical Substitution for Interpretable Lexical Semantic Change Detection
We propose a solution for the LSCDiscovery shared task on Lexical Semantic Change Detection in Spanish.
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Lexical substitution, i. e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc.
The Document Vectors Using Cosine Similarity Revisited
The results show that while RoBERTa has a clear advantage for larger training sets, the DV-ngrams-cosine performs better than RoBERTa when the labelled training set is very small (10 or 20 documents).
Ranked #7 on
Sentiment Analysis
on IMDb
LIORI at SemEval-2021 Task 2: Span Prediction and Binary Classification approaches to Word-in-Context Disambiguation
This paper presents our approaches to SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation task.
SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task
In our experiments, we used a neural system based on the XLM-R, a pre-trained transformer-based masked language model, as a baseline.
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings
To verify this hypothesis we developed a solution for the Multilingual and Cross-lingual Word-in-Context (MCL-WiC) task, that does not use any of the shared task data or other WiC data for training.
LIORI at SemEval-2021 Task 8: Ask Transformer for measurements
This work describes our approach for subtasks of SemEval-2021 Task 8: MeasEval: Counts and Measurements which took the official first place in the competition.
Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction
This paper describes our approach to {``}DeftEval: Extracting Definitions from Free Text in Textbooks{''} competition held as a part of Semeval 2020.
BOS at SemEval-2020 Task 1: Word Sense Induction via Lexical Substitution for Lexical Semantic Change Detection
The first solution performs word sense induction (WSI) first, then makes the decision based on the induced word senses.
Combining Neural Language Models for WordSense Induction
Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word.
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extraction, data augmentation, etc.
Cross-lingual Named Entity List Search via Transliteration
Out-of-vocabulary words are still a challenge in cross-lingual Natural Language Processing tasks, for which transliteration from source to target language or script is one of the solutions.
Combining Lexical Substitutes in Neural Word Sense Induction
Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning.
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction
We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2.
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
We present our system for semantic frame induction that showed the best performance in Subtask B. 1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019).
How much does a word weigh? Weighting word embeddings for word sense induction
The paper describes our participation in the first shared task on word sense induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018).
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language.
Human and Machine Judgements for Russian Semantic Relatedness
On the one hand, humans easily make judgments about semantic relatedness.
Making Sense of Word Embeddings
We present a simple yet effective approach for learning word sense embeddings.
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
We present a new approach to extraction of hypernyms based on projection learning and word embeddings.
