1,992 research outputs found
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
Leveraging literals for knowledge graph embeddings
Wissensgraphen (Knowledge Graphs, KGs) reprĂ€sentieren strukturierte Fakten, die sich aus EntitĂ€ten und den zwischen diesen bestehenden Relationen zusammensetzen. Um die Effizienz von KG-Anwendungen zu maximieren, ist es von Vorteil, KGs in einen niedrigdimensionalen Vektorraum zu transformieren. KGs folgen dem Paradigma einer offenen Welt (Open World Assumption, OWA), d. h. fehlende Information wird als potenziell möglich angesehen, wodurch ihre Verwendung in realen Anwendungsszenarien oft eingeschrĂ€nkt wird. Link-Vorhersage (Link Prediction, LP) zur VervollstĂ€ndigung von KGs kommt daher eine hohe Bedeutung zu. LP kann in zwei unterschiedlichen Modi durchgefĂŒhrt werden, transduktiv und induktiv, wobei die erste Möglichkeit voraussetzt, dass alle EntitĂ€ten der Testdaten in den Trainingsdaten vorhanden sind, wĂ€hrend die zweite Möglichkeit auch zuvor nicht bekannte EntitĂ€ten in den Testdaten zulĂ€sst. Die vorliegende Arbeit untersucht die Verwendung von Literalen in der transduktiven und induktiven LP, da KGs zahlreiche numerische und textuelle Literale enthalten, die eine wesentliche Semantik aufweisen. Zur Evaluierung dieser LP Methoden werden spezielle Benchmark-DatensĂ€tze eingefĂŒhrt.
Insbesondere wird eine neuartige KG Embedding (KGE) Methode, RAILD, vorgeschlagen, die Textliterale zusammen mit kontextuellen Graphinformationen fĂŒr die LP nutzt. Das Ziel von RAILD ist es, die bestehende ForschungslĂŒcke beim Lernen von Embeddings fĂŒr beim Training ungesehene Relationen zu schlieĂen. DafĂŒr wird eine Architektur vorgeschlagen, die Sprachmodelle (Language Models, LMs) mit Netzwerkembeddings kombiniert. Hierzu erfolgt ein Feintuning von leistungsstarken vortrainierten LMs wie BERT zum Zweck der LP, wobei textuelle Beschreibungen von EntitĂ€ten und Relationen genutzt werden. DarĂŒber hinaus wird ein neuer Algorithmus, WeiDNeR, eingefĂŒhrt, um ein Relationsnetzwerk zu generieren, das zum Erlernen graphbasierter Embeddings von Relationen unter Verwendung eines Netzwerkembeddingsmodells dient. Die VektorreprĂ€sentationen dieser Relationen werden fĂŒr die LP kombiniert. Zudem wird ein weiteres neuartiges Embeddingmodell, LitKGE, vorgestellt, das numerische Literale fĂŒr die transduktive LP verwendet. Es zielt darauf ab, numerische Merkmale fĂŒr EntitĂ€ten durch Graphtraversierung zu erzeugen. HierfĂŒr wird ein weiterer Algorithmus, WeiDNeR_Extended, eingefĂŒhrt, der ein Netzwerk aus Objekt- und Datentypproperties erzeugt. Aus den aus diesem Netzwerk extrahierten Propertypfaden werden dann numerische Merkmale von EntitĂ€ten generiert.
Des Weiteren wird der Einsatz eines mehrsprachigen LM zur Kodierung von EntitĂ€tenbeschreibungen in verschiedenen natĂŒrlichen Sprachen zum Zweck der LP untersucht. FĂŒr die Evaluierung der KGE-Modelle wurden die Benchmark-DatensĂ€tze LiterallyWikidata und Wikidata68K erstellt. Die vielversprechenden Ergebnisse, die mit den vorgestellten Modellen erzielt wurden, eröffnen interessante Fragestellungen fĂŒr die zukĂŒnftige Forschung auf dem Gebiet der KGEs und ihrer Folgeanwendungen
Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations
Dagstuhl Seminar 15201 was conducted on âCross-Lingual Cross-Media Content Linking: Annotations and Joint Representationsâ. Participants from around the world participated in the seminar and presented state-of-the-art and ongoing research related to the seminar topic. An executive summary of the seminar, abstracts of the talks from participants and working group discussions are presented in the forthcoming sections
Visual Pivoting for (Unsupervised) Entity Alignment
This work studies the use of visual semantic representations to align
entities in heterogeneous knowledge graphs (KGs). Images are natural components
of many existing KGs. By combining visual knowledge with other auxiliary
information, we show that the proposed new approach, EVA, creates a holistic
entity representation that provides strong signals for cross-graph entity
alignment. Besides, previous entity alignment methods require human labelled
seed alignment, restricting availability. EVA provides a completely
unsupervised solution by leveraging the visual similarity of entities to create
an initial seed dictionary (visual pivots). Experiments on benchmark data sets
DBP15k and DWY15k show that EVA offers state-of-the-art performance on both
monolingual and cross-lingual entity alignment tasks. Furthermore, we discover
that images are particularly useful to align long-tail KG entities, which
inherently lack the structural contexts necessary for capturing the
correspondences.Comment: To appear at AAAI-202
A survey on knowledge-enhanced multimodal learning
Multimodal learning has been a field of increasing interest, aiming to
combine various modalities in a single joint representation. Especially in the
area of visiolinguistic (VL) learning multiple models and techniques have been
developed, targeting a variety of tasks that involve images and text. VL models
have reached unprecedented performances by extending the idea of Transformers,
so that both modalities can learn from each other. Massive pre-training
procedures enable VL models to acquire a certain level of real-world
understanding, although many gaps can be identified: the limited comprehension
of commonsense, factual, temporal and other everyday knowledge aspects
questions the extendability of VL tasks. Knowledge graphs and other knowledge
sources can fill those gaps by explicitly providing missing information,
unlocking novel capabilities of VL models. In the same time, knowledge graphs
enhance explainability, fairness and validity of decision making, issues of
outermost importance for such complex implementations. The current survey aims
to unify the fields of VL representation learning and knowledge graphs, and
provides a taxonomy and analysis of knowledge-enhanced VL models
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Multimodality Representation Learning, as a technique of learning to embed
information from different modalities and their correlations, has achieved
remarkable success on a variety of applications, such as Visual Question
Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision
Language Retrieval (VLR). Among these applications, cross-modal interaction and
complementary information from different modalities are crucial for advanced
models to perform any multimodal task, e.g., understand, recognize, retrieve,
or generate optimally. Researchers have proposed diverse methods to address
these tasks. The different variants of transformer-based architectures
performed extraordinarily on multiple modalities. This survey presents the
comprehensive literature on the evolution and enhancement of deep learning
multimodal architectures to deal with textual, visual and audio features for
diverse cross-modal and modern multimodal tasks. This study summarizes the (i)
recent task-specific deep learning methodologies, (ii) the pretraining types
and multimodal pretraining objectives, (iii) from state-of-the-art pretrained
multimodal approaches to unifying architectures, and (iv) multimodal task
categories and possible future improvements that can be devised for better
multimodal learning. Moreover, we prepare a dataset section for new researchers
that covers most of the benchmarks for pretraining and finetuning. Finally,
major challenges, gaps, and potential research topics are explored. A
constantly-updated paperlist related to our survey is maintained at
https://github.com/marslanm/multimodality-representation-learning
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