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A Tensor based Factorization Model of Semantic Compositionality

A Tensor based Factorization Model of Semantic Compositionality

... of compositionality, often attributed to Frege, is the principle that states that the meaning of a complex expression is a function of the meaning of its parts and the way those parts are (syntactically) combined ... See full document

10

Semantic Based Multilingual Document Clustering via Tensor Modeling

Semantic Based Multilingual Document Clustering via Tensor Modeling

... As previously discussed in Section 2.1, BabelNet provides WSD algorithms for multilingual corpora. More specifically, the authors in (Navigli and Ponzetto, 2012b) suggest to use the Degree algorithm (Navigli and Lapata, ... See full document

10

Dual Tensor Model for Detecting Asymmetric Lexico Semantic Relations

Dual Tensor Model for Detecting Asymmetric Lexico Semantic Relations

... Distributional methods detect asymmetric rela- tions using only distributional vectors of words as input. Distributional models come in both unsuper- vised and supervised flavors. Unsupervised metrics for hypernymy ... See full document

11

Determining Compositionality of Word Expressions Using Word Space Models

Determining Compositionality of Word Expressions Using Word Space Models

... Evaluation based on ranking can be realized by measuring ranked correlations (Spearman and Kendall) or Precision/Recall scores and curves com- monly used ... See full document

9

Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality

Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality

... distributional model can capture the prediction of the semantic compositionality of German ...perceived semantic compositionality with high levels of statistical ...of ... See full document

8

A Markovian approach to distributional semantics with application to semantic compositionality

A Markovian approach to distributional semantics with application to semantic compositionality

... this model by proposing to represent each node in a parse tree by a vector capturing the meaning and a matrix capturing the compositional ...the tensor and the Kronecker product of the vectors representing ... See full document

10

TuckER: Tensor Factorization for Knowledge Graph Completion

TuckER: Tensor Factorization for Knowledge Graph Completion

... facts based on existing ...linear model based on Tucker decomposition of the binary tensor represen- tation of knowledge graph ...pressive model, derive sufficient bounds on its ... See full document

10

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

... proposed model is inspired by the success of the factorized regression techniques developed in recommendation systems (Yang, Zhao, and Gao 2017; Xu, Zhou, and Tan ...able tensor factorizations were proposed ... See full document

8

Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System

Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System

... presented based on tensor factorization that is extended from matrix factor- ization for three dimensions or ...use tensor decomposition model in recommender ...dimensional ... See full document

5

A Non negative Tensor Factorization Model for Selectional Preference Induction

A Non negative Tensor Factorization Model for Selectional Preference Induction

... These are not the only sensible dimensions that have been found by the algorithm. A quick qual- itative evaluation indicates that about 44 dimen- sions contain similar, framelike semantics. In an- other 43 dimensions, ... See full document

8

A Generalized Language Model in Tensor Space

A Generalized Language Model in Tensor Space

... n-gram model and we prove that TSLM is a gen- eralization of ...language model is based on the word frequency statistics and does not have the semantic advantages that word vectors in ... See full document

9

Representing annotation compositionality and provenance for the Semantic Web

Representing annotation compositionality and provenance for the Semantic Web

... to model multiple genes affecting a ...Our model would en- able identifying and distinguishing epistasis relationships determined on the basis of one variant analysis from those based on another ... See full document

15

Computing Semantic Compositionality in Distributional Semantics

Computing Semantic Compositionality in Distributional Semantics

... the semantic spaces (HAL and ...PLS model is highly positively associated with the observed ...corpus- based test set. Finally, although both semantic spaces (HAL and RI) produce the same ... See full document

10

Measuring MWE compositionality using semantic annotation

Measuring MWE compositionality using semantic annotation

... LSA-based model for measuring the decomposability of MWEs by ex- amining the similarity between them and their constituent words, with higher similarity indicat- ing the greater ...their model on ... See full document

10

Knowledge Graph Completion via Complex Tensor Factorization

Knowledge Graph Completion via Complex Tensor Factorization

... matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, ... See full document

38

Predicting the Semantic Compositionality of Prefix Verbs

Predicting the Semantic Compositionality of Prefix Verbs

... our model. Van den Bosch and Daelemans (1999) use memory-based learning to analyze ...WordFrame model includes a prefixation compo- ...is based on a log-linear model that can include ... See full document

11

Latent Semantic Tensor Indexing for Community based Question Answering

Latent Semantic Tensor Indexing for Community based Question Answering

... The earlier studies mainly focus on generat- ing redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead ... See full document

6

Multi way Tensor Factorization for Unsupervised Lexical Acquisition

Multi way Tensor Factorization for Unsupervised Lexical Acquisition

... the semantic preferences verbs have for their ...2010), semantic role labeling (Bharati et ...2011), semantic role labeling (Gildea and Jurafsky, 2002; Zapirain et ... See full document

18

A Data Driven, Factorization Parser for CCG Dependency Structures

A Data Driven, Factorization Parser for CCG Dependency Structures

... the model introduced in (Martins and Almeida, ...decomposition based joint model for joint syntactic and semantic ...shallow semantic representation, i.e. Semantic Role Labeling, ... See full document

11

Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality

Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality

... It is remarkable that even with K as small as 100, the r-RNTN approaches the performance of the RNTN with a small fraction of the parameters. This reinforces our hypothesis that complex trans- formation modeling afforded ... See full document

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