multi-sense
Real Multi Sense or Pseudo Multi Sense: An Approach to Improve Word Representation
... which sense a word belongs to based on its ...that multi-sense word embeddings could be helpful to improve the performance on many NLP and NLU ...
10
Implicit Subjective and Sentimental Usages in Multi sense Word Embeddings
... for sense clus- tering to decide senses are so sensitive to contex- tual variation and usage of word, therefore may embed a single sense into several ...
6
Using Multi Sense Vector Embeddings for Reverse Dictionaries
... creating multi-sense vector embeddings have been ...DeConf multi-sense embedding for integrating them in a downstream ...(not sense vectors) as a supervised sequence prediction task ...
12
Applying Multi Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non Literal Language
... Up to date, the majority of computational models still determines the semantic relat- edness between words (or larger linguis- tic units) on the type level. In this pa- per, we compare and extend ...
8
Ontologically Grounded Multi sense Representation Learning for Semantic Vector Space Models
... Similarity Scoring in Context: As outlined by Reisinger and Mooney (2010), multi-sense VSMs can be used to consider context when computing similarity between words. We use the SCWS dataset (Huang et al., ...
11
A multi-sense approach to information reception and knowledge creation in learning
... concepts (Schnotz, 2002). An appropriate analogy would be the way Dynamic Programming approaches solving reasonably complex problems by breaking them down to a number of sub-problems, solving these smaller problems and ...
10
Syntax Aware Multi Sense Word Embeddings for Deep Compositional Models of Meaning
... As our compositional architectures we use a RecNN and an RNN. In the RecNN case, the words are composed by following the result of an external parser, while for the RNN the composi- tion takes place in sequence from left ...
12
Bilingual Learning of Multi sense Embeddings with Discrete Autoencoders
... with sense induction as a separate, clustering problem that is followed by an embedding learning compo- nent (Huang et ...the sense assignment and the em- beddings are trained jointly (Neelakantan et ...
11
Do Multi Sense Embeddings Improve Natural Language Understanding?
... As far as possible we try to perform an apple- to-apple comparison on these tasks, and our goal is an analytic one—to investigate how well se- mantic information can be encoded in multi-sense embeddings and ...
11
Beyond Bilingual: Multi sense Word Embeddings using Multilingual Context
... the sense clusters and embeddings by using ...a multi-sense variant of the skip- gram model which learns the different number of sense vectors for all words from a large mono- lingual corpus ...
10
Constructing High Quality Sense specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi sense
... pseudo multi-sense with the help of WordNet (Miller, 1995), and then trained a linear transformation which aims to maximize the similarity of detected pseudo ...pseudo multi-sense detection ...
5
Evaluating multi sense embeddings for semantic resolution monolingually and in word translation
... evaluating multi-sense word embeddings (MSEs) where pol- ysemous words have multiple vectors, ideally one per ...on sense distinctions made in traditional monolingual ...
7
Probabilistic FastText for Multi Sense Word Embeddings
... We introduce Probabilistic FastText, a new model for word embeddings that can cap- ture multiple word senses, sub-word struc- ture, and uncertainty information. In particular, we represent each word with a Gaussian ...
11
Multi sense Embeddings through a Word Sense Disambiguation Process
... homonymy. Multi-sense word embeddings were devised to alleviate these and other problems by repre- senting each word-sense separately, but stud- ies in this area are still in its infancy and much can ...
15
Multi Sense Embeddings from Topic Models
... trained sense and word embeddings separately, with sense specific word embeddings computed as a weighted sum of the two, where the weights are calculated using topic ...the sense-specific word ...
8
On Modeling Sense Relatedness in Multi prototype Word Embedding
... Clustering-based multi- Sense Embedding model), that models the relat- edness among word senses by using the fuzzy clustering based method for word sense induc- tion, and then learns sense ...
10
Making sense of electrical sense in crayfish
... to sense an animal before it is seen or smelt would assist with capturing prey or escaping a predator, and being able to navigate without needing physical landmarks would provide efficient travel through sparse ...
7
A Comprehensive Study of Using 2D Barcode for Multi Robot Labelling and Communication
... There are many approaches that have been taken in order to undertake this problem. The prominent approach is by using wireless radio communication by means of radio frequency, Wireless Sensor Network (WSN), specific ...
5
Bridge Text and Knowledge by Learning Multi Prototype Entity Mention Embedding
... Entity linking is a core NLP task of identifying the reference entity for mentions in texts. The main difficulty lies in the ambiguity of various en- tities sharing the same mention phrase. Previous work addressed this ...
11