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Found 47 papers, 18 papers with code
Multilingual ELMo and the Effects of Corpus Sampling
Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages.
SemEval 2022 Task 10: Structured Sentiment Analysis
In this paper, we introduce the first SemEval shared task on Structured Sentiment Analysis, for which participants are required to predict all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity.
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.
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis
We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations.
NorBench -- A Benchmark for Norwegian Language Models
We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics.
Trained on 100 million words and still in shape: BERT meets British National Corpus
While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source -- the British National Corpus.
RuDSI: graph-based word sense induction dataset for Russian
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian.
Contextualized language models for semantic change detection: lessons learned
Our findings show that contextualized methods can often predict high change scores for words which are not undergoing any real diachronic semantic shift in the lexicographic sense of the term (or at least the status of these shifts is questionable).
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change
In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes.
NorDiaChange: Diachronic Semantic Change Dataset for Norwegian
We describe NorDiaChange: the first diachronic semantic change dataset for Norwegian.
Grammatical Profiling for Semantic Change Detection
Semantics, morphology and syntax are strongly interdependent.
Three-part diachronic semantic change dataset for Russian
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval.
Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks
Often approaches based on simple rules outperform or come close to the results of the notorious pre-trained language models like GPT-3 or BERT.
Ranked #3 on
Common Sense Reasoning
on RWSD
Large-Scale Contextualised Language Modelling for Norwegian
We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training.
Representing ELMo embeddings as two-dimensional text online
We describe a new addition to the WebVectors toolkit which is used to serve word embedding models over the Web.
RuSemShift: a dataset of historical lexical semantic change in Russian
We present RuSemShift, a large-scale manually annotated test set for the task of semantic change modeling in Russian for two long-term time period pairs: from the pre-Soviet through the Soviet times and from the Soviet through the post-Soviet times.
ELMo and BERT in semantic change detection for Russian
We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data.
UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1.
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages.
\'UFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing
This paper describes the {\'U}FAL--Oslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. 2019).
To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation
Then, these models were evaluated on the word sense disambiguation task.
Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian
We measure the intensity of diachronic semantic shifts in adjectives in English, Norwegian and Russian across 5 decades.
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists.
Making Fast Graph-based Algorithms with Graph Metric Embeddings
The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs.
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselines
The paper introduces manually annotated test sets for the task of tracing diachronic (temporal) semantic shifts in Russian.
Learning Graph Embeddings from WordNet-based Similarity Measures
We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities.
Diachronic word embeddings and semantic shifts: a survey
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models.
Russian word sense induction by clustering averaged word embeddings
The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018).
Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus
Aside from the already known fact that the RNC is generally a better training corpus than web corpora, we enumerate and explain fine differences in how the models process semantic similarity task, what parts of the evaluation set are difficult for particular models and why.
Tracing armed conflicts with diachronic word embedding models
Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words.
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words.
Redefining Context Windows for Word Embedding Models: An Experimental Study
Distributional semantic models learn vector representations of words through the contexts they occur in.
Clustering of Russian Adjective-Noun Constructions using Word Embeddings
This paper presents a method of automatic construction extraction from a large corpus of Russian.
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit
In this demo we present WebVectors, a free and open-source toolkit helping to deploy web services which demonstrate and visualize distributional semantic models (widely known as word embeddings).
Exploration of register-dependent lexical semantics using word embeddings
We present an approach to detect differences in lexical semantics across English language registers, using word embedding models from distributional semantics paradigm.
Neural Embedding Language Models in Semantic Clustering of Web Search Results
In this paper, a new approach towards semantic clustering of the results of ambiguous search queries is presented.
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints
We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus.
Texts in, meaning out: neural language models in semantic similarity task for Russian
Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics.
Semantic clustering of Russian web search results: possibilities and problems
The paper deals with word sense induction from lexical co-occurrence graphs.
