[PDF] Top 20 Towards Incremental Learning of Word Embeddings Using Context Informativeness
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Towards Incremental Learning of Word Embeddings Using Context Informativeness
... and incremental evaluation setups on the definitional dataset in Table 1: MR 955 vs 81,705±14,076 and MRR ...high learning rate at every test iteration, moving their original meaning to- wards the semantics ... See full document
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K Embeddings: Learning Conceptual Embeddings for Words using Context
... K- Embeddings results versus the results of a relaxed evaluation for CBOW, which considers the top K embeddings instead of the ...K embeddings, the overall (combined) performance for different K ... See full document
6
Word Context Character Embeddings for Chinese Word Segmentation
... by using the base classifier itself, and tri-training (Zhou and Li, 2005; Li et ...semi-supervised learning method in NLP is knowledge distillation, which extracts knowledge from large-scale auto-labeled ... See full document
7
Learning Composition Models for Phrase Embeddings
... use word-specific/tensor trans- formations to force the interactions between com- ponent words in a ...methods, word- specific transformations, which are usually matri- ces, are learned for a subset of ... See full document
16
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
... unsupervised learning: the lack of supervision between a word and those appearing in the associ- ated ...the context are not related to the considered ...any context of the training corpora is ... See full document
10
Querying Word Embeddings for Similarity and Relatedness
... each word has led to the suggestion that distinct representations, and perhaps even distinct learn- ing models, are needed for optimal performance on these distinct tasks (Mandera et ...as word embedding ... See full document
10
Beyond Context: A New Perspective for Word Embeddings
... Framing word prediction as an XML problem allows us to define a unifying framework for word ...training word em- beddings using lessons from the XML ...standard word embedding ... See full document
11
Beyond Bilingual: Multi sense Word Embeddings using Multilingual Context
... Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP ...a word, by ex- ploiting crosslingual signals to aid sense ...multi-sense ... See full document
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Joint Learning of Sense and Word Embeddings
... the word vector ac- cording to the context, MSSG predicts the nearest sense first, and then updates the gradient of the sense ...of word sense dis- crimination by clustering a word contexts, ... See full document
7
Learning Gender Neutral Word Embeddings
... Word Embeddings Word embeddings serve as a fundamental building block for a broad range of NLP applications (dos Santos and Gatti, 2014; Bahdanau et ... See full document
7
Learning Multilingual Word Embeddings Using Image Text Data
... ing word contexts (and subword information, in the case of MUSE) in a large monolingual cor- pus, whereas we use only images as a bridge be- tween words within and across ...the word “democracy”), it is ... See full document
10
Multimodal Frame Identification with Multilingual Evaluation
... just learning the data majority: our trained models have better F1-macro and especially much higher ambiguous F1-macro scores with ...timodal context representations improve results compared to unimodal ... See full document
11
Learning to Respond to Mixed code Queries using Bilingual Word Embeddings
... while using parallel cor- pus data as our training ...(its context) by giving a set of sentences (also called corpus), and the model loops on the words of each sentence and learn relation between words in a ... See full document
5
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... a word has been rep- resented using a high-dimensional sparse Bag-of- Words (BoW) ...in word embeddings, where words meanings are represented by low-dimensional and dense vectors using ... See full document
7
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
... of embeddings (word embeddings and context ...the context embeddings can also be considered as word embeddings, since the vo- cabulary set of words and contexts are ... See full document
11
The Role of Context Types and Dimensionality in Learning Word Embeddings
... of word embeddings learned with different types of context, on a wide range of intrinsic simi- larity and relatedness tasks, and extrinsic NLP tasks, namely dependency parsing, named entity recogni- ... See full document
11
Learning Word Embeddings without Context Vectors
... We train word embeddings on the English Wikipedia dump. We preprocess this dump us- ing gensim.corpora.wikicorpus package 3 and take a subsample of 100K articles. Our corpus consists of approximately 175M ... See full document
6
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs
... bilingual word embedding space can be in- duced by projecting monolingual word embed- ding spaces from two languages using a self- learning paradigm without any bilingual super- ...for ... See full document
13
Neural context embeddings for automatic discovery of word senses
... representing word instances and their con- ...representing context words using a novel weighting schema consisting of a semantic component, and a temporal component, see Section ...for using ... See full document
8
Domain Adapted Word Embeddings for Improved Sentiment Classification
... combine word embeddings weighted by their term frequency ...DA embeddings obtained by applying KCCA on GlvCC generic and LSA DS embeddings pro- vide the best performing results on all data ... See full document
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