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JRC-Names: Multilingual Entity Name variants and titles as Linked Data

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Learning computationally efficient static word and sentence representations

Prakhar Gupta

Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, sta ...

EPFL2021

Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...

MICROTOME PUBL2021

CLEF-HIPE-2020 Shared Task Named Entity Datasets

Maud Ehrmann, Matteo Romanello

CLEF-HIPE-2020 (Identifying Historical People, Places and other Entities) is a evaluation campaign on named entity processing on historical newspapers in French, German and English, which was organized in the context of the impresso project and run as a CL ...

2020

Named Entity Processing for Historical Texts

Maud Ehrmann, Matteo Romanello

Recognition and identification of real-world entities is at the core of virtually any text mining application. As a matter of fact, referential units such as names of persons, locations and organizations underlie the semantics of texts and guide their inte ...

2019

deepschema.org: An Ontology for Typing Entities in the Web of Data

Karl Aberer, Michele Catasta, Panagiotis Smeros, Amit Gupta

Discovering the appropriate type of an entity in the Web of Data is still considered an open challenge, given the complexity of the many tasks it entails. Among them, the most notable is the definition of a generic and cross-domain ontology. While the onto ...

2017

A Method for Record Linkage with Sparse Historical Data

Maud Ehrmann, Yannick Rochat, Giovanni Colavizza

Massive digitization of archival material, coupled with automatic document processing techniques and data visualisation tools offers great opportunities for reconstructing and exploring the past. Unprecedented wealth of historical data (e.g. names of perso ...

2016

Named Entity Resources - Overview and Outlook

Maud Ehrmann

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and t ...

European Language Resources Association (ELRA)2016

Kamusi Pre:D – Lexicon-based source-side predisambiguation for MT and other text processing applications

Martin Benjamin

Kamusi has been developing a system to analyze texts on the source side and present users with sense-specified dictionary options. Similarly to spellcheck, the user selects the intended meaning. We then use a multilingual lexical database to bridge to matc ...

ENeL2016

Contextualized ranking of entity types based on knowledge graphs

Karl Aberer, Philippe Cudré-Mauroux, Michele Catasta, Roman Prokofyev

A large fraction of online queries targets entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet ...

Elsevier Science Bv2016

Building Word Embeddings for Solving Natural Language Processing

Rémi Philippe Lebret

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

École Polytechnique Fédérale de Lausanne2016