[PDF] Top 20 Learning Distributed Representations of Texts and Entities from Knowledge Base
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Learning Distributed Representations of Texts and Entities from Knowledge Base
... In order to investigate what happens inside our model, we conducted a qualitative analysis using our proposed representations trained with sentences. We first inspected the word representations of our model ... See full document
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Multi level Representations for Fine Grained Typing of Knowledge Base Entities
... for learning entity representation are: (i) links and descriptions in KB, (ii) name and contexts in ...con- texts in corpora, but we also include (Wikipedia) ...represent entities on three levels: ... See full document
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Linking Entities to a Knowledge Base with Query Expansion
... con- texts and global world knowledge to expand query language ...named entities in the local con- texts and explore a positional language model to weigh them differently based on their dis- ... See full document
10
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
... vector representations of text meta-data on a novel data set of legal texts that includes case, statutory, and administrative ...any entities producing text, and used for recommendations, ... See full document
6
Learning to Link Entities with Knowledge Base
... unstructured texts, the task is to link this entity with an entry stored in the existing knowledge ...Previous learning based solutions mainly fo- cus on classification ...a learning to rank ... See full document
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A Generative Entity Mention Model for Linking Entities with Knowledge Base
... a knowledge base usually contains millions of entities, it is time- consuming to compute all P(m,e) scores between a name mention and all the entities contained in a knowledge ...anchor ... See full document
10
Jointly Embedding Entities and Text with Distant Supervision
... Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applica- ...jointly learning embeddings of entities and text ... See full document
12
Toward Socially Infused Information Extraction: Embedding Authors, Mentions, and Entities
... By learning the semantic interactions be- tween the author embeddings and the pre-trained Freebase entity embeddings, the entity linking sys- tem can incorporate more disambiguating context from the social ... See full document
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Structural Representations for Learning Relations between Pairs of Texts
... A description of RTE can be found in (Giampic- colo et al., 2007): it is defined as a directional relation extraction between two text fragments, called text and hypothesis. The implication is sup- posed to be detectable ... See full document
11
Discovering Implicit Knowledge with Unary Relations
... of knowledge-level supervision, sometimes called distant supervision, to train a deep learning based ...a knowledge base and an unannotated ...of entities in the ...feature ... See full document
10
Learning Distributed Representations of Sentences from Unlabelled Data
... generated from symbolic ...data from the corrupted ...DAE representations (as a ‘pre-training’ or initialisation step) gives more robust (supervised) classification performance in deep feedforward ... See full document
11
Learning Distributed Representations for Multilingual Text Sequences
... its knowledge to predict the N -grams in the sequence, and conversely, if a vector can con- tribute well to the task, then one can think of it as the representation of the ...the distributed representation ... See full document
7
Representing Text for Joint Embedding of Text and Knowledge Bases
... of knowledge base and textual in- formation was first shown to outperform either source alone in the framework of path-ranking al- gorithms in a combined knowledge base and text graph (Lao et ... See full document
11
Generating Logical Forms from Graph Representations of Text and Entities
... of entities, we achieve similar results as Jia and Liang (2016) when also ablating their data augmentation method, as shown in Table ...ablating entities com- pletely, our architecture essentially reduces ... See full document
12
Semantic Enrichment Across Language: A Case Study of Czech Bibliographic Databases
... This paper deals with semantic enrichment of textual resources by means of automat- ically generated named entity recognizers- linkers and advanced indexing and search- ing mechanisms that can be integrated into various ... See full document
10
Building Compact Entity Embeddings Using Wikidata
... A large number of possible words that are encountered in a natural language text suggest that a Natural Language Processing (NLP) model is always expected to encounter new word sequences that have never been seen during ... See full document
9
Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction
... for learning complex structures in data, such as those occurring in musical se- ...of learning fea- tures from the data at multiple levels of ... See full document
7
A Competency Knowledge-Base for BIM Learning
... Building Information Modelling (BIM) is the current expression of technical and procedural innovation within the construction industry. It is a methodology for generating, exchanging and managing a constructed facility’s ... See full document
10
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering
... Our evidence representation module is based on the ideas of memory networks (Sukhbaatar et al., 2015; Kumar et al., 2015; Miller et al., 2016), which also embed relevant information into a vec- tor space. However, they ... See full document
6
Extraction of Entities from Web with Knowledge Mining
... data from multiple entries in table of database to be clustered with same ...output from the clustering algorithm provides the average distance from cluster members to the center of each ...obtained ... See full document
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