[PDF] Top 20 Embedding Semantic Relations into Word Representations
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Embedding Semantic Relations into Word Representations
... learn word representations as a by- product, the main focus on language modeling is to predict the next word in a sentence given the previous words, and not on learning word ... See full document
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Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
... for word relatedness is a lexicon, such as Word- Net (Miller, 1995) or Roget’s Thesaurus (Kipfer, ...VSM word vectors using a ...multi- word terms as well as missing relationships be- tween ... See full document
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Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation?
... multiple representations. Some of the works extract lists of relations from knowledge bases and use those to either modify the learning algorithms (Halawi et ...pre-trained word ... See full document
11
Composition of Word Representations Improves Semantic Role Labelling
... of semantic role labelling (SRL) is to discover the relations that hold between a pred- icate and its arguments in a given input sen- tence ...This semantic knowl- edge at the predicate-argument ... See full document
7
Classification of Semantic Paraphasias: Optimization of a Word Embedding Model
... participant. Semantic impairment is measured by a clinician’s rating of semantic simi- larity between the target-production pairs, and in- volves a defined similarity criteria involving syn- onymy, ... See full document
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Improving Sparse Word Representations with Distributional Inference for Semantic Composition
... neural word embeddings (Mikolov et ...distributional semantic knowledge into the vec- tor space by leveraging the intrinsic structure of the distributional ...richer word representations and ... See full document
12
Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations
... syntagmatic relations among the words, which relate the words that co-occur in the same text ...distributional representations of words based on the text region that the words occur in, as exemplified by ... See full document
10
Conditional Generators of Words Definitions
... Continuous representations of words are used in many natural language processing (NLP) applica- ...high-quality word em- beddings are most effective if not millions of training examples are available, which ... See full document
6
Evaluating distributed word representations for capturing semantics of biomedical concepts
... vector representations of words using huge corpus in unsupervised man- ...Such word vector representations, also known as word embedding, have been shown to improve the performance of ... See full document
6
Joint Semantic and Distributional Word Representations with Multi Graph Embeddings
... Another line of research, closer to our proposi- tion, is to incorporate semantic knowledge at train- ing time. Liu et al. (2015) do so through using or- dinal constraints (similarity of synonyms should be higher ... See full document
6
Semantic Frame Identification with Distributed Word Representations
... takes word embeddings as input and learns to identify semantic ...A word embedding is a distributed representa- tion of meaning where each word is represented as a vector in R n ...Such ... See full document
11
Conceptor Debiasing of Word Representations Evaluated on WEAT
... the semantic content of the word em- beddings, we examined conceptor debiased em- bedding for semantic similarity ...dard word similarity test set and report Pearson’s correlation. The ... See full document
9
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings
... or word sequences of arbitrary length are embedded into the same continuous vector ...computing semantic relat- edness among text fragments by using the induced similarity measures in that feature ...these ... See full document
9
A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding
... of word mean- ings by human ...influence word meanings, while Pulverm¨uller (1999) argues that concepts are grounded in complex simulations of physical and introspective events, activating the frontal re- ... See full document
10
Short Text Semantic Similarity using Glove Word Embedding
... that word in the ...that word appears in a given text. Representation of words known as word embedding is the another example, more related to this ...project. Word embedding are ... See full document
6
Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789 1914
... the word juive is especially in- teresting. The word drumont entered its space in the time period ...the semantic space of the word juive there are different words related to ...theatre ... See full document
11
Pattern based Word Sketches for the Extraction of Semantic Relations
... non-hierarchical relations have also been studied and implement- ed as KPs, each has their own ...hierarchical relations, such as generic-specific, cause has many different subtypes (Marshman ... See full document
10
Word Embeddings as Metric Recovery in Semantic Spaces
... current word embedding algorithms build on the distributional hypothesis (Harris, 1954) where similar contexts imply similar meanings so as to tie co-occurrences of words to their underlying mean- ...free ... See full document
14
Using Graphs for Word Embedding with Enhanced Semantic Relations
... a word embedding algorithm with semantic ...the semantic relations between ...proposed embedding did not achieve the best results on analogy and classification tasks but was ... See full document
10
Neural Mechanisms for Combinatorial Semantics in Language and Vision: Evidence From FMRI, Patients, and Brain Stimulation
... Perceptual model. We constructed a model of perceptual color similarity using subjective evaluations collected in a separate norming survey (N=18). This model captures color similarity independent of object categories. ... See full document
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