24 research outputs found
Using Embeddings for Both Entity Recognition and Linking in Tweets
L’articolo descrive la nostra partecipazione al task di Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) a Evalita 2016. Il nostro approccio si basa sull’utilizzo di un Named Entity tagger che sfrutta embeddings sia character-level che word-level. I primi consentono di apprendere le idiosincrasie della scrittura nei tweet. L’uso di un tagger completo consente di riconoscere uno spettro più ampio di entità rispetto a quelle conosciute per la loro presenza in Knowledge Base o gazetteer. Le prove sottomesse hanno ottenuto il primo, secondo e quarto dei punteggi ufficiali.The paper describes our sub-missions to the task on Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) at Evalita 2016. Our approach relies on a technique of Named Entity tagging that exploits both charac-ter-level and word-level embeddings. Character-based embeddings allow learn-ing the idiosyncrasies of the language used in tweets. Using a full-blown Named Entity tagger allows recognizing a wider range of entities than those well known by their presence in a Knowledge Base or gazetteer. Our submissions achieved first, second and fourth top offi-cial scores
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Knowledge Graph Construction in Power Distribution Networks
In this paper, we propose a method for knowledge graph construction in power
distribution networks. This method leverages entity features, which involve
their semantic, phonetic, and syntactic characteristics, in both the knowledge
graph of distribution network and the dispatching texts. An enhanced model
based on Convolutional Neural Network, is utilized for effectively matching
dispatch text entities with those in the knowledge graph. The effectiveness of
this model is evaluated through experiments in real-world power distribution
dispatch scenarios. The results indicate that, compared with the baselines, the
proposed model excels in linking a variety of entity types, demonstrating high
overall accuracy in power distribution knowledge graph construction task
Wikification of Concept Mentions within Spoken Dialogues Using Domain Constraints from Wikipedia
Abstract While most previous work on Wikification has focused on written texts, this paper presents a Wikification approach for spoken dialogues. A set of analyzers are proposed to learn dialogue-specific properties along with domain knowledge of conversations from Wikipedia. Then, the analyzed properties are used as constraints for generating candidates, and the candidates are ranked to find the appropriate links. The experimental results show that our proposed approach can significantly improve the performances of the task in human-human dialogues