11,460 research outputs found
Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon
This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are
extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Museums as disseminators of niche knowledge: Universality in accessibility for all
Accessibility has faced several challenges within audiovisual translation Studies and gained great opportunities for its establishment as a methodologically and theoretically well-founded discipline. Initially conceived as a set of services and practices that provides access to audiovisual media content for persons with sensory impairment, today accessibility can be viewed as a concept involving more and more universality thanks to its contribution to the dissemination of audiovisual products on the topic of marginalisation. Against this theoretical backdrop, accessibility is scrutinised from the perspective of aesthetics of migration and minorities within the field of the visual arts in museum settings. These aesthetic narrative forms act as modalities that encourage the diffusion of ‘niche’ knowledge, where processes of translation and interpretation provide access to all knowledge as counter discourse. Within this framework, the ways in which language is used can be considered the beginning of a type of local grammar in English as lingua franca for interlingual translation and subtitling, both of which ensure access to knowledge for all citizens as a human rights principle and regardless of cultural and social differences. Accessibility is thus gaining momentum as an agent for the democratisation and transparency of information against media discourse distortions and oversimplifications
The Zero Resource Speech Challenge 2017
We describe a new challenge aimed at discovering subword and word units from
raw speech. This challenge is the followup to the Zero Resource Speech
Challenge 2015. It aims at constructing systems that generalize across
languages and adapt to new speakers. The design features and evaluation metrics
of the challenge are presented and the results of seventeen models are
discussed.Comment: IEEE ASRU (Automatic Speech Recognition and Understanding) 2017.
Okinawa, Japa
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