Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)(About) > new simple, yet effective approaches to
learn domain specific word embeddings.
> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).
> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a lowdimensional
## Related work (Learning Word Embeddings with Priors)
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model (Yu and Dredze 2014)
combines CBOW and RTM through linear combination
Linking Folksonomies and Ontologies for Supporting Knowledge Sharing: a State of the Art(About) Social tagging systems have recently become very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This report compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.
Projet ISICIL : Intégration Sémantique de l'Information par des Communautés d'Intelligence en Ligne
International Workshop on Emergent Semantics and Ontology Evolution(About) The Semantic Web and collaborative tagging are two complementary approaches...Bundles, classification, relations or tagging of tags are some promising ways to enforce some kinds of structure for tags in order to enable scalability and findability...There is a growing interest in marrying the two paradigms in order to create large-scale semantic and intelligent content.
Abstract submissions: July 23, submissions due: July 30