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[PDF] Top 20 Unsupervised Learning of Distributional Relation Vectors

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Unsupervised Learning of Distributional Relation Vectors

Unsupervised Learning of Distributional Relation Vectors

... tion vectors can be used in various ways to enrich the input to neural network ...with relation vectors encoding their relationship to the differ- ent words from the ...of vectors expressing ... See full document

11

The CogALex V Shared Task on the Corpus Based Identification of Semantic Relations

The CogALex V Shared Task on the Corpus Based Identification of Semantic Relations

... exploited Distributional Semantic Models (DSMs), either of the count-based or word-embedding type (Baroni et ...machine learning classifiers ...extracting distributional properties with ... See full document

11

Deriving Boolean structures from distributional vectors

Deriving Boolean structures from distributional vectors

... Boolean vectors, en- forcing feature inclusion in Boolean space for the entailing ...inclusion relation, our method is radically different from recent super- vised approaches that learn an entailment ... See full document

14

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

... erence, relation extraction, and question an- swering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning ... See full document

11

Cross Topic Distributional Semantic Representations Via Unsupervised Mappings

Cross Topic Distributional Semantic Representations Via Unsupervised Mappings

... 2 Unsupervised methods induce mul- tiple word representations without leveraging se- mantic lexical ...of vectors assigned to each ...“sense-specific” vectors for each target ...embedding ... See full document

10

Non distributional Word Vector Representations

Non distributional Word Vector Representations

... representation learning for words is a technique of central importance in ...such vectors tend to consist of uninter- pretable components whose relationship to the categories of traditional lexical seman- ... See full document

6

Specializing Distributional Vectors of All Words for Lexical Entailment

Specializing Distributional Vectors of All Words for Lexical Entailment

... versarial unsupervised model fine-tuned with the closed-form Procustes solution (Conneau et ...an unsupervised self-learning algorithm that iteratively bootstraps new bilingual seeds, ini- tialized ... See full document

12

Collocation Classification with Unsupervised Relation Vectors

Collocation Classification with Unsupervised Relation Vectors

... learn vectors that cap- ture the relation between concepts, typically using distributional statistics from sentences mention- ing both words (Espinosa-Anke and Schockaert, 2018; Washio and Kato, ... See full document

8

Squibs: When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics

Squibs: When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics

... with vectors tracking co- occurrence in corpora (Turney and Pantel ...compositional distributional semantic models (CDSMs) estimate degrees of seman- tic similarity (or, more generally, relatedness) between ... See full document

10

Distributional Composition using Higher Order Dependency Vectors

Distributional Composition using Higher Order Dependency Vectors

... of distributional fea- tures derived from word ...the distributional feature iobj:fill, where iobj denotes the inverse indirect object grammati- cal ... See full document

10

Vectors or Graphs? On Differences of Representations for Distributional Semantic Models

Vectors or Graphs? On Differences of Representations for Distributional Semantic Models

... of vectors and their dimensions is one of the strongest points of critique on dense vectors: while sometimes, post-hoc explanations for some of the dimensions are found, it holds in general that most or ... See full document

7

Unsupervised Word Sense Induction using Distributional Statistics

Unsupervised Word Sense Induction using Distributional Statistics

... an unsupervised manner using a large and unbiased corpus, and tune the granularity governing parameters for different downstream tasks which require sense ... See full document

9

Determining Compositionality of Word Expressions Using Word Space Models

Determining Compositionality of Word Expressions Using Word Space Models

... added vectors for the examined expressions to WSM in such a way that the original vectors for words were ...WSM vectors of their ...WSM vectors of “short” and “distance” since the numbers of ... See full document

9

Unsupervised Detecting and Locating of Gastrointestinal Anomalies

Unsupervised Detecting and Locating of Gastrointestinal Anomalies

... In this paper, the technique of detection and localization of gastrointestinal anomalies is put forth. An attempt has been made to contemplate the significance of various medical diagnosis systems that have been proposed ... See full document

9

Using Optimized Distributional Parameters as Inputs in a Sequential Unsupervised and Supervised Modeling of Sunspots Data

Using Optimized Distributional Parameters as Inputs in a Sequential Unsupervised and Supervised Modeling of Sunspots Data

... an unsupervised stage whereby the al- gorithm searches through the data for optimal parameter values and a supervised stage that adapts the parameters for predictive ...Gaussian distributional proper- ties ... See full document

8

Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods

Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods

... a distributional thesaurus (DT) that is computed, based on the dis- tributional hypothesis (Harris, 1951), using a mono- lingual background corpus and does not require any language-specific rules or ... See full document

6

Unsupervised Type and Token Identification of Idiomatic Expressions

Unsupervised Type and Token Identification of Idiomatic Expressions

... the distributional similarity between the expression and its constituents (Baldwin et ...fixedness, distributional similarity, and collocation-based measures into a set of features which are used to rank ... See full document

44

A Review of Unsupervised Artificial Neural Networks with Applications

A Review of Unsupervised Artificial Neural Networks with Applications

... Unsupervised learning also performs the task of reducing the number of variables in high-dimensional data, a process known as dimensionality ...by unsupervised artificial neural network algorithms ... See full document

5

Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors

Probabilistic Domain Modelling With Contextualized Distributional Semantic Vectors

... Vector space models form the basis of modern information retrieval (Salton et al., 1975), but only recently have distributional models been proposed that are compositional (Mitchell and Lapata, 2008; Clark et al., ... See full document

10

Unsupervised Information Extraction with Distributional Prior Knowledge

Unsupervised Information Extraction with Distributional Prior Knowledge

... the unsupervised IE task, where slot fillers correspond to observations in the model, and their labels correspond to hidden variables we want to ...machine learning literature, researchers have explored the ... See full document

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