Automatic Identification and Disambiguation of Concepts and Named Entities in the Multilingual Wikipedia

Scozzafava, Federico; Raganato, Alessandro; Moro, Andrea; Navigli, Roberto

doi:10.1007/978-3-319-24309-2_27

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…To learn our embeddings, we adapt the freely available sample of 500k articles of Babelfied English Wikipedia (Scozzafava et al, 2015). To our knowledge, this is one of the largest published and evaluated sense-annotated corpora, containing over 500 million words, of which over 100 million are annotated with Babel synsets, with an estimated synset annotation accuracy of 77.8%.…”

Section: Building Supersense Embeddingsmentioning

confidence: 99%

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Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

Flek¹,

Gurevych²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks.

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Section: Building Supersense Embeddingsmentioning

confidence: 99%

“…Similarly to , they find that SemCor may not be a sufficient resource for supersense tagging adaption to different domains. Therefore, in our work, we explore the potential of using an automatically annotated Babelfied Wikipedia corpus (Scozzafava et al, 2015) for this task.…”

Section: Supersense Taggingmentioning

confidence: 99%

Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

Flek¹,

Gurevych²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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“…5 Babel-Net is a large multilingual encyclopedic dictionary and semantic network, comprising approximately 16 million entries for concepts and named entities linked by semantic relations. As training corpus we used the English portion of BabelWiki, 6 a multilingual corpus comprising the English Wikipedia (Scozzafava et al, 2015). The corpus was automatically annotated with named entities and concepts using Babelfy (Moro et al, 2014), a state-ofthe-art disambiguation and entity linking system, based on the BabelNet semantic network.…”

Section: Implementation Detailsmentioning

confidence: 99%

LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

Iacobacci¹,

Navigli²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i.e., senses, of words. In this paper we explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task. We release the code and the resulting word and sense embeddings at http://lcl.uniroma1. it/LSTMEmbed.

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“…However, hand-labeled sense annotations are notoriously difficult to obtain on a large scale, and manually curated corpora (Miller et al, 1993;Passonneau et al, 2012) have a limited size. Given that scaling the manual annotation process becomes practically unfeasible when both lexicographic and encyclopedic knowledge is addressed (Schubert, 2006), recent years have witnessed efforts to produce larger sense-annotated corpora automatically (Moro et al, 2014a;Taghipour and Ng, 2015a;Scozzafava et al, 2015;Raganato et al, 2016). Even though these automatic approaches produce noisier corpora, it has been shown that training on them leads to better supervised and semi-supervised models (Taghipour and Ng, 2015b;Raganato et al, 2016;Yuan et al, 2016;Raganato et al, 2017), as well as to effective embedded representations for senses (Iacobacci et al, 2015;Flekova and Gurevych, 2016).…”

Section: Introductionmentioning

confidence: 99%

EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

Bovi

Camacho-Collados

Raganato

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.

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Automatic Identification and Disambiguation of Concepts and Named Entities in the Multilingual Wikipedia

Cited by 9 publications

References 18 publications

Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

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