Papers by Luis Espinosa Anke
GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary (2025.coling-main)
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| Challenge: | Effective RD methods have applications in accessibility, translation or writing support systems. |
| Approach: | They propose a simple approach to RD that leverages LLMs and embedding models to obtain the most relevant word or set of words given a textual description or definition. |
| Outcome: | The proposed approach outperforms baselines in well studied RD datasets while showing less overfitting. |
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning (2020.lrec-1)
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| Challenge: | Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. |
| Approach: | They propose to evaluate multiple cross-lingual word embedding models and compare their strengths and limitations to evaluate their effectiveness. |
| Outcome: | The proposed models perform well with noisy text and language pairs with major differences. |
The interplay between lexical resources and Natural Language Processing (N18-6)
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| Challenge: | linguistic, world and common sense knowledge is an important research area, but processing and storing it in lexical resources is not a straightforward task. |
| Approach: | They propose to use NLP methods to help process of constructing and enriching lexical resources and the use of lexicals for improving NLP applications. |
| Outcome: | The proposed approach aims to speed up and/or ease up the process of resource curation and enrichment. |
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond (2022.lrec-1)
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| Challenge: | Language models are ubiquitous in NLP, but current analyses focus on (multilingual variants of) standard benchmarks and task-specific corpora as multilingual signals. |
| Approach: | They propose a model to train and evaluate multilingual language models in Twitter using a set of Twitter datasets in eight different languages and a XLM-T model. |
| Outcome: | The proposed model trains and evaluates multilingual models on Twitter. |
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)
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| Challenge: | Modern NLP systems are typically ill-equipped when applied to noisy user-generated text. |
| Approach: | They propose a new evaluation framework consisting of seven Twitter-specific classification tasks. |
| Outcome: | The proposed framework is based on seven heterogeneous Twitter-specific classification tasks. |
Pre-Training Language Models for Identifying Patronizing and Condescending Language: An Analysis (2022.lrec-1)
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| Challenge: | Patronizing and Condescending Language (PCL) is a subtle but harmful type of discourse. |
| Approach: | They propose to pre-train PCL detection models on other NLP tasks to improve their detection . they find that performance gains are possible when pre-training on sentiment, harmful language and commonsense morality. |
| Outcome: | The proposed models improve on pre-training on other NLP tasks focusing on sentiment, harmful language and commonsense morality, compared with tasks concentrating on political speech and social justice, the authors show . |
TimeLMs: Diachronic Language Models from Twitter (2022.acl-demo)
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| Challenge: | Neural language models (LMs) are a key enabler in NLP, but lack of diachronic specialization affects both the ability to generalize to future data and the reliability of experimental results. |
| Approach: | They propose to use Twitter data to develop time-specific language models that are specialized on the time variable. |
| Outcome: | The proposed models cope with trends and peaks in activity involving specific named entities or concept drift. |
Sentence Selection Strategies for Distilling Word Embeddings from BERT (2022.lrec-1)
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| Challenge: | Using language models to learn word embeddings is a key feature of transformer-based language models. |
| Approach: | They propose to use language models to learn high-quality word vectors from as few as 5 to 10 sentences with a careful selection strategy. |
| Outcome: | The proposed strategies can learn high-quality word vectors from as few as 5 to 10 sentences. |
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies? (2021.acl-long)
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| Challenge: | Analogies play a central role in human commonsense reasoning. |
| Approach: | They analyze the capabilities of transformer-based language models on an unsupervised task . they find off-the-shelf language models can identify analogies to a certain extent . |
| Outcome: | The proposed language models outperform word embedding models on an unsupervised task . the best results were obtained with GPT-2 and RoBERTa . |
Collocation Classification with Unsupervised Relation Vectors (P19-1)
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| Challenge: | Existing methods for relation classification are based on word embeddings, but they pose a greater challenge than syntactic and semantic relations. |
| Approach: | They propose a distributional landscape based on word embeddings as a suitable basis for relation classification of collocations . they also conduct experiments on a subset of this benchmark . |
| Outcome: | The proposed dataset is compared to the well known DiffVec dataset and shows that it is more efficient than the standard methods. |
Probing Pre-Trained Language Models for Disease Knowledge (2021.findings-acl)
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| Challenge: | Pre-trained language models perform medical reasoning tasks, but standard benchmarks lack examples that require such forms of reasoning. |
| Approach: | They propose a medical reasoning benchmark that uses pre-trained language models to analyze medical reasoning in the biomedical domain. |
| Outcome: | The proposed benchmarks are based on pre-trained language models that perform medical reasoning tasks. |
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers (2022.starsem-1)
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| Challenge: | lexical collocations exhibit varying degrees of frozenness due to their varying degree of frozenncy. |
| Approach: | They propose a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture and evaluate the task of collocation recognition in context. |
| Outcome: | The proposed model encoding syntactic dependencies is useful, and provides insights on differences in collocation typification in English, Spanish and French. |
Relational Word Embeddings (P19-1)
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| Challenge: | Existing approaches to learn word embeddings rely on external knowledge bases . however, they are limited by the amount of available relational knowledge . |
| Approach: | They propose to encode relational knowledge in a separate word embedding . this is complementary to a standard word embedded from co-occurrence statistics . |
| Outcome: | The proposed word embedding is complementary to a standard word embed. |
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
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Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
| Outcome: | The proposed models can extract metaphors from literary texts without using domain experts. |
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)
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Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, Eugenio Martínez Cámara
| Challenge: | TweetNLP is an integrated platform for natural language processing in social media. |
| Approach: | They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks. |
| Outcome: | The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. |
Evaluating language models for the retrieval and categorization of lexical collocations (2021.eacl-main)
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| Challenge: | Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items. |
| Approach: | They perform an exhaustive analysis of current language models for collocation understanding . they first construct a dataset of apparitions of lexical collocations in context . |
| Outcome: | The proposed models perform well in distinguishing light verb constructions, especially if the collocation’s first argument acts as subject, but often fail to distinguish, first, different syntactic structures within the same semantic category, and second, fine-grained semantic categories which restrict the use of small sets of valid collocates for a given base. |
WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models (2024.lrec-main)
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| Challenge: | Lexical resources are a repository of knowledge and are used for many tasks, including word sense disambiguation and etymology. |
| Approach: | They compare WordNet, the most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
| Outcome: | The most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities (2020.coling-main)
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| Challenge: | a new dataset is proposed to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Approach: | They propose to annotate a dataset to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Outcome: | The proposed dataset supports the development of NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
RAGAs: Automated Evaluation of Retrieval Augmented Generation (2024.eacl-demo)
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| Challenge: | RAGAs are a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. |
| Approach: | They propose a framework for reference-free evaluation of Retrieval Augmented Generation pipelines. |
| Outcome: | RAGAs can be used to evaluate RAG pipelines without human annotations . the framework can be useful for faster evaluation cycles given the fast adoption of LLMs based on human annotation. |
Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction (2022.starsem-1)
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| Challenge: | Earlier approaches to taxonomy learning focused on mining lexico-syntactic patterns from candidate pairs. |
| Approach: | They propose to use prompts to distill knowledge from language models to refine methods . they also show that linguistic properties of prompts dictate downstream performance . |
| Outcome: | The proposed methods outperform some supervised strategies and are competitive with the current state-of-the-art under adequate conditions. |
AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
| Approach: | They propose to learn a unified embedding space in which all three types of representations can be integrated. |
| Outcome: | The proposed model outperforms existing approaches in ontology completion tasks. |