Prodromos Malakasiotis

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Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely(More)
This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and(More)
This paper presents three methods that can be used to recognize paraphrases. They all employ string similarity measures applied to shallow abstractions of the input sentences, and a Maximum Entropy classifier to learn how to combine the resulting features. Two of the methods also exploit WordNet to detect synonyms and one of them also exploits a dependency(More)
Question answering systems aim to find answers to natural language questions by searching in document collections (e.g., repositories of scientific articles or the entire Web) and/or structured data (e.g., databases, ontologies). Strictly speaking, the answer to a question might sometimes be simply ‘yes’ or ‘no’, a named entity, or a set of named entities.(More)
We propose a document retrieval method for question answering that represents documents and questions as weighted centroids of word embeddings and reranks the retrieved documents with a relaxation of Word Mover’s Distance. Using biomedical questions and documents from BIOASQ, we show that our method is competitive with PUBMED. With a top-k approximation,(More)
This paper describes the systems with which we participated in the task Sentiment Analysis in Twitter of SEMEVAL 2013 and specifically the Message Polarity Classification. We used a 2-stage pipeline approach employing a linear SVM classifier at each stage and several features including BOW features, POS based features and lexicon based features. We have(More)
This paper describes aueb’s participation in tac 2008. Specifically, we participated in the summarization and textual entailment recognition tracks. For the former we trained a Support Vector Regression model that is used to rank the summary’s candidate sentences; and for the latter we used a Maximum Entropy classifier along with string similarity measures(More)
Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia 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(More)
This paper describes the system submitted for the Sentiment Analysis in Twitter Task of SEMEVAL 2014 and specifically the Message Polarity Classification subtask. We used a 2–stage pipeline approach employing a linear SVM classifier at each stage and several features including morphological features, POS tags based features and lexicon based features.