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Found 27 papers, 15 papers with code
TextGraphs 2022 Shared Task on Natural Language Premise Selection
In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.
TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration
While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.
TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration
In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task.
YARN: Spinning-in-Progress
YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing.
Eliminating Fuzzy Duplicates in Crowdsourced Lexical Resources
Collaboratively created lexical resources is a trending approach to creating high quality thesauri in a short time span at a remarkably low price.
Toloka Visual Question Answering Benchmark
In this paper, we present Toloka Visual Question Answering, a new crowdsourced dataset allowing comparing performance of machine learning systems against human level of expertise in the grounding visual question answering task.
Best Prompts for Text-to-Image Models and How to Find Them
Recent progress in generative models, especially in text-guided diffusion models, has enabled the production of aesthetically-pleasing imagery resembling the works of professional human artists.
Clustering Without Knowing How To: Application and Evaluation
Crowdsourcing allows running simple human intelligence tasks on a large crowd of workers, enabling solving problems for which it is difficult to formulate an algorithm or train a machine learning model in reasonable time.
CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription
The main obstacle towards designing aggregation methods for more advanced applications is the absence of training data, and in this work, we focus on bridging this gap in speech recognition.
Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial
In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex.
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.
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration
While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited.
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).
Unsupervised Sense-Aware Hypernymy Extraction
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction.
Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains.
Unsupervised Semantic Frame Induction using Triclustering
We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction.
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context.
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.
RUSSE: The First Workshop on Russian Semantic Similarity
The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference.
Improving Hypernymy Extraction with Distributional Semantic Classes
In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms.
Human and Machine Judgements for Russian Semantic Relatedness
On the one hand, humans easily make judgments about semantic relatedness.
Fighting with the Sparsity of Synonymy Dictionaries
Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph.
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.
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.
Watset: Automatic Induction of Synsets from a Graph of Synonyms
This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings.
Towards crowdsourcing and cooperation in linguistic resources
Linguistic resources can be populated with data through the use of such approaches as crowdsourcing and gamification when motivated people are involved.
