Ion Androutsopoulos


2021

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MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
Ilias Chalkidis | Manos Fergadiotis | Ion Androutsopoulos
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. We highlight the effect of temporal concept drift and the importance of chronological, instead of random splits. We use the dataset as a testbed for zero-shot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target). We find that fine-tuning a multilingually pretrained model (XLM-ROBERTA, MT5) in a single source language leads to catastrophic forgetting of multilingual knowledge and, consequently, poor zero-shot transfer to other languages. Adaptation strategies, namely partial fine-tuning, adapters, BITFIT, LNFIT, originally proposed to accelerate fine-tuning for new end-tasks, help retain multilingual knowledge from pretraining, substantially improving zero-shot cross-lingual transfer, but their impact also depends on the pretrained model used and the size of the label set.

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Proceedings of the Natural Legal Language Processing Workshop 2021
Nikolaos Aletras | Ion Androutsopoulos | Leslie Barrett | Catalina Goanta | Daniel Preotiuc-Pietro
Proceedings of the Natural Legal Language Processing Workshop 2021

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EDGAR-CORPUS: Billions of Tokens Make The World Go Round
Lefteris Loukas | Manos Fergadiotis | Ion Androutsopoulos | Prodromos Malakasiotis
Proceedings of the Third Workshop on Economics and Natural Language Processing

We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.

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SemEval-2021 Task 5: Toxic Spans Detection
John Pavlopoulos | Jeffrey Sorensen | Léo Laugier | Ion Androutsopoulos
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gold toxic spans provided by the organisers. It could also be treated as rationale extraction, using classifiers trained on potentially larger external datasets of posts manually annotated as toxic or not, without toxic span annotations. For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans. Participants submitted their predicted spans for a held-out test set and were scored using character-based F1. This overview summarises the work of the 36 teams that provided system descriptions.

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Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases
Ilias Chalkidis | Manos Fergadiotis | Dimitrios Tsarapatsanis | Nikolaos Aletras | Ion Androutsopoulos | Prodromos Malakasiotis
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their decisions, similar to those of humans, by requiring the models to satisfy additional constraints. To this end, we introduce a new application on legal text where, contrary to mainstream literature targeting word-level rationales, we conceive rationales as selected paragraphs in multi-paragraph structured court cases. We also release a new dataset comprising European Court of Human Rights cases, including annotations for paragraph-level rationales. We use this dataset to study the effect of already proposed rationale constraints, i.e., sparsity, continuity, and comprehensiveness, formulated as regularizers. Our findings indicate that some of these constraints are not beneficial in paragraph-level rationale extraction, while others need re-formulation to better handle the multi-label nature of the task we consider. We also introduce a new constraint, singularity, which further improves the quality of rationales, even compared with noisy rationale supervision. Experimental results indicate that the newly introduced task is very challenging and there is a large scope for further research.

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Context Sensitivity Estimation in Toxicity Detection
Alexandros Xenos | John Pavlopoulos | Ion Androutsopoulos
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on current datasets will also disregard context, making the detection of context-sensitive toxicity a lot harder when it occurs. We constructed and publicly release a dataset of 10k posts with two kinds of toxicity labels per post, obtained from annotators who considered (i) both the current post and the previous one as context, or (ii) only the current post. We introduce a new task, context-sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered. Using the new dataset, we show that systems can be developed for this task. Such systems could be used to enhance toxicity detection datasets with more context-dependent posts or to suggest when moderators should consider the parent posts, which may not always be necessary and may introduce additional costs.

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A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections
Dimitris Pappas | Ion Androutsopoulos
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or other snippets of the top-ranked documents, and (iv) select spans of the top-ranked snippets as exact answers. Pipelines are conceptually simple, but errors propagate from one component to the next, without later components being able to revise earlier decisions. We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents. The architecture is general and can be used with any neural text relevance ranker. We experiment with two main instantiations of the architecture, based on POSIT-DRMM (PDRMM) and a BERT-based ranker. Experiments on biomedical data from BIOASQ show that our joint models vastly outperform the pipelines in snippet retrieval, the main goal for QA, with fewer trainable parameters, also remaining competitive in document retrieval. Furthermore, our joint PDRMM-based model is competitive with BERT-based models, despite using orders of magnitude fewer parameters. These claims are also supported by human evaluation on two test batches of BIOASQ. To test our key findings on another dataset, we modified the Natural Questions dataset so that it can also be used for document and snippet retrieval. Our joint PDRMM-based model again outperforms the corresponding pipeline in snippet retrieval on the modified Natural Questions dataset, even though it performs worse than the pipeline in document retrieval. We make our code and the modified Natural Questions dataset publicly available.

2020

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Toxicity Detection: Does Context Really Matter?
John Pavlopoulos | Jeffrey Sorensen | Lucas Dixon | Nithum Thain | Ion Androutsopoulos
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Moderation is crucial to promoting healthy online discussions. Although several ‘toxicity’ detection datasets and models have been published, most of them ignore the context of the posts, implicitly assuming that comments may be judged independently. We investigate this assumption by focusing on two questions: (a) does context affect the human judgement, and (b) does conditioning on context improve performance of toxicity detection systems? We experiment with Wikipedia conversations, limiting the notion of context to the previous post in the thread and the discussion title. We find that context can both amplify or mitigate the perceived toxicity of posts. Moreover, a small but significant subset of manually labeled posts (5% in one of our experiments) end up having the opposite toxicity labels if the annotators are not provided with context. Surprisingly, we also find no evidence that context actually improves the performance of toxicity classifiers, having tried a range of classifiers and mechanisms to make them context aware. This points to the need for larger datasets of comments annotated in context. We make our code and data publicly available.

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BioMRC: A Dataset for Biomedical Machine Reading Comprehension
Dimitris Pappas | Petros Stavropoulos | Ion Androutsopoulos | Ryan McDonald
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

We introduceBIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.

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An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels
Ilias Chalkidis | Manos Fergadiotis | Sotiris Kotitsas | Prodromos Malakasiotis | Nikolaos Aletras | Ion Androutsopoulos
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications and presents interesting challenges. First, not all labels are well represented in the training set, due to the very large label set and the skewed label distributions of datasets. Also, label hierarchies and differences in human labelling guidelines may affect graph-aware annotation proximity. Finally, the label hierarchies are periodically updated, requiring LMTC models capable of zero-shot generalization. Current state-of-the-art LMTC models employ Label-Wise Attention Networks (LWANs), which (1) typically treat LMTC as flat multi-label classification; (2) may use the label hierarchy to improve zero-shot learning, although this practice is vastly understudied; and (3) have not been combined with pre-trained Transformers (e.g. BERT), which have led to state-of-the-art results in several NLP benchmarks. Here, for the first time, we empirically evaluate a battery of LMTC methods from vanilla LWANs to hierarchical classification approaches and transfer learning, on frequent, few, and zero-shot learning on three datasets from different domains. We show that hierarchical methods based on Probabilistic Label Trees (PLTs) outperform LWANs. Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWAN. Finally, we propose new models that leverage the label hierarchy to improve few and zero-shot learning, considering on each dataset a graph-aware annotation proximity measure that we introduce.

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LEGAL-BERT: The Muppets straight out of Law School
Ilias Chalkidis | Manos Fergadiotis | Prodromos Malakasiotis | Nikolaos Aletras | Ion Androutsopoulos
Findings of the Association for Computational Linguistics: EMNLP 2020

BERT has achieved impressive performance in several NLP tasks. However, there has been limited investigation on its adaptation guidelines in specialised domains. Here we focus on the legal domain, where we explore several approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets. Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain. Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains. These are: (a) use the original BERT out of the box, (b) adapt BERT by additional pre-training on domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific corpora. We also propose a broader hyper-parameter search space when fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.

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Domain Adversarial Fine-Tuning as an Effective Regularizer
Giorgos Vernikos | Katerina Margatina | Alexandra Chronopoulou | Ion Androutsopoulos
Findings of the Association for Computational Linguistics: EMNLP 2020

In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.

2019

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ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT
John Pavlopoulos | Nithum Thain | Lucas Dixon | Ion Androutsopoulos
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents the application of two strong baseline systems for toxicity detection and evaluates their performance in identifying and categorizing offensive language in social media. PERSPECTIVE is an API, that serves multiple machine learning models for the improvement of conversations online, as well as a toxicity detection system, trained on a wide variety of comments from platforms across the Internet. BERT is a recently popular language representation model, fine tuned per task and achieving state of the art performance in multiple NLP tasks. PERSPECTIVE performed better than BERT in detecting toxicity, but BERT was much better in categorizing the offensive type. Both baselines were ranked surprisingly high in the SEMEVAL-2019 OFFENSEVAL competition, PERSPECTIVE in detecting an offensive post (12th) and BERT in categorizing it (11th). The main contribution of this paper is the assessment of two strong baselines for the identification (PERSPECTIVE) and the categorization (BERT) of offensive language with little or no additional training data.

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SUM-QE: a BERT-based Summary Quality Estimation Model
Stratos Xenouleas | Prodromos Malakasiotis | Marianna Apidianaki | Ion Androutsopoulos
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose SUM-QE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SUM-QE achieves very high correlations with human ratings, outperforming simpler models addressing these linguistic aspects. Predictions of the SUM-QE model can be used for system development, and to inform users of the quality of automatically produced summaries and other types of generated text.

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Neural Legal Judgment Prediction in English
Ilias Chalkidis | Ion Androutsopoulos | Nikolaos Aletras
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case’s facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT’s length limitation.

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Large-Scale Multi-Label Text Classification on EU Legislation
Ilias Chalkidis | Emmanouil Fergadiotis | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EUR-LEX, annotated with ∼4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT’s maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.

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A Survey on Biomedical Image Captioning
John Pavlopoulos | Vasiliki Kougia | Ion Androutsopoulos
Proceedings of the Second Workshop on Shortcomings in Vision and Language

Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians. This article is the first survey of biomedical image captioning, discussing datasets, evaluation measures, and state of the art methods. Additionally, we suggest two baselines, a weak and a stronger one; the latter outperforms all current state of the art systems on one of the datasets.

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Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation
Ilias Chalkidis | Emmanouil Fergadiotis | Prodromos Malakasiotis | Nikolaos Aletras | Ion Androutsopoulos
Proceedings of the Natural Legal Language Processing Workshop 2019

We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union’s public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.

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Transfer Learning for Causal Sentence Detection
Manolis Kyriakakis | Ion Androutsopoulos | Artur Saudabayev | Joan Ginés i Ametllé
Proceedings of the 18th BioNLP Workshop and Shared Task

We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer learning, namely ELMO and BERT, using a bidirectional GRU with self-attention ( BIGRUATT ) as a baseline. We experiment with both generic public relation extraction datasets and a new biomedical causal sentence detection dataset, a subset of which we make publicly available. We find that transfer learning helps only in very small datasets. With larger datasets, BIGRUATT reaches a performance plateau, then larger datasets and transfer learning do not help.

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Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Sotiris Kotitsas | Dimitris Pappas | Ion Androutsopoulos | Ryan McDonald | Marianna Apidianaki
Proceedings of the 18th BioNLP Workshop and Shared Task

Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.

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SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Christos Baziotis | Ion Androutsopoulos | Ioannis Konstas | Alexandros Potamianos
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQˆ3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.

2018

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Deep Relevance Ranking Using Enhanced Document-Query Interactions
Ryan McDonald | George Brokos | Ion Androutsopoulos
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR’s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

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Obligation and Prohibition Extraction Using Hierarchical RNNs
Ilias Chalkidis | Ion Androutsopoulos | Achilleas Michos
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each sentence to an embedding, and processes the sentence embeddings to classify each sentence. Apart from being faster to train, the hierarchical BILSTM outperforms the flat one, even when the latter considers surrounding sentences, because the hierarchical model has a broader discourse view.

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BioRead: A New Dataset for Biomedical Reading Comprehension
Dimitris Pappas | Ion Androutsopoulos | Haris Papageorgiou
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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AUEB at BioASQ 6: Document and Snippet Retrieval
George Brokos | Polyvios Liosis | Ryan McDonald | Dimitris Pappas | Ion Androutsopoulos
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

We present AUEB’s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.

2017

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Deeper Attention to Abusive User Content Moderation
John Pavlopoulos | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Experimenting with a new dataset of 1.6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams. We also compare against a CNN operating on word embeddings, and a word-list baseline. A novel, deep, classificationspecific attention mechanism improves the performance of the RNN further, and can also highlight suspicious words for free, without including highlighted words in the training data. We consider both fully automatic and semi-automatic moderation.

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Deep Learning for User Comment Moderation
John Pavlopoulos | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the First Workshop on Abusive Language Online

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of EnglishWikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.

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Improved Abusive Comment Moderation with User Embeddings
John Pavlopoulos | Prodromos Malakasiotis | Juli Bakagianni | Ion Androutsopoulos
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.

2016

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Using Centroids of Word Embeddings and Word Mover’s Distance for Biomedical Document Retrieval in Question Answering
Georgios-Ioannis Brokos | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Maria Pontiki | Dimitris Galanis | Haris Papageorgiou | Ion Androutsopoulos | Suresh Manandhar | Mohammad AL-Smadi | Mahmoud Al-Ayyoub | Yanyan Zhao | Bing Qin | Orphée De Clercq | Véronique Hoste | Marianna Apidianaki | Xavier Tannier | Natalia Loukachevitch | Evgeniy Kotelnikov | Nuria Bel | Salud María Jiménez-Zafra | Gülşen Eryiğit
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis
Stavros Giorgis | Apostolos Rousas | John Pavlopoulos | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis
Dionysios Xenos | Panagiotis Theodorakakos | John Pavlopoulos | Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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SemEval-2015 Task 12: Aspect Based Sentiment Analysis
Maria Pontiki | Dimitris Galanis | Haris Papageorgiou | Suresh Manandhar | Ion Androutsopoulos
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method
John Pavlopoulos | Ion Androutsopoulos
Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)

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SemEval-2014 Task 4: Aspect Based Sentiment Analysis
Maria Pontiki | Dimitris Galanis | John Pavlopoulos | Harris Papageorgiou | Ion Androutsopoulos | Suresh Manandhar
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Multi-Granular Aspect Aggregation in Aspect-Based Sentiment Analysis
John Pavlopoulos | Ion Androutsopoulos
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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Using Integer Linear Programming in Concept-to-Text Generation to Produce More Compact Texts
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Using Integer Linear Programming for Content Selection, Lexicalization, and Aggregation to Produce Compact Texts from OWL Ontologies
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 14th European Workshop on Natural Language Generation

2012

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Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression
Dimitrios Galanis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of COLING 2012

2011

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A Generate and Rank Approach to Sentence Paraphrasing
Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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A New Sentence Compression Dataset and Its Use in an Abstractive Generate-and-Rank Sentence Compressor
Dimitrios Galanis | Ion Androutsopoulos
Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop

2010

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An extractive supervised two-stage method for sentence compression
Dimitrios Galanis | Ion Androutsopoulos
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Finding Short Definitions of Terms on Web Pages
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protege and Second Life
Dimitrios Galanis | George Karakatsiotis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the Demonstrations Session at EACL 2009

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Adaptive Natural Language Interaction
Stasinos Konstantopoulos | Athanasios Tegos | Dimitrios Bilidas | Ion Androutsopoulos | Gerasimos Lampouras | Colin Matheson | Olivier Deroo | Prodromos Malakasiotis
Proceedings of the Demonstrations Session at EACL 2009

2007

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Learning Textual Entailment using SVMs and String Similarity Measures
Prodromos Malakasiotis | Ion Androutsopoulos
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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Generating Multilingual Descriptions from Linguistically Annotated OWL Ontologies: the NaturalOWL System
Dimitrios Galanis | Ion Androutsopoulos
Proceedings of the Eleventh European Workshop on Natural Language Generation (ENLG 07)

2005

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Exploiting OWL Ontologies in the Multilingual Generation of Object Descriptions
Ion Androutsopoulos | Spyros Kallonis | Vangelis Karkaletsis
Proceedings of the Tenth European Workshop on Natural Language Generation (ENLG-05)

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A Practically Unsupervised Learning Method to Identify Single-Snippet Answers to Definition Questions on the Web
Ion Androutsopoulos | Dimitrios Galanis
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Learning to Identify Single-Snippet Answers to Definition Questions
Spyridoula Miliaraki | Ion Androutsopoulos
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Learning to Order Facts for Discourse Planning in Natural Language Generation
Aggeliki Dimitromanolaki | Ion Androutsopoulos
Proceedings of the 9th European Workshop on Natural Language Generation (ENLG-2003) at EACL 2003

2002

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Ellogon: A New Text Engineering Platform
Georgios Petasis | Vangelis Karkaletsis | Georgios Paliouras | Ion Androutsopoulos | Constantine D. Spyropoulos
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Stacking Classifiers for Anti-Spam Filtering of E-Mail
Georgios Sakkis | Ion Androutsopoulos | Georgios Paliouras | Vangelis Karkaletsis | Constantine D. Spyropoulos | Panagiotis Stamatopoulos
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing

2000

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Selectional Restrictions in HPSG
Ion Androutsopoulos | Robert Dale
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

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