[PDF] Top 20 Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
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Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
... pre-trained word embed- dings outperform random word embeddings by a large ...pre-trained word embeddings are highly use- ful for text classification (Iyyer et ...DEPS context ... See full document
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Learning Syntactic Categories Using Paradigmatic Representations of Word Context
... the learning algorithm. The first subgroup represents each word type with its context vector and clusters these vectors accordingly (Sch¨utze, ...of word types (Glober- son et ... See full document
12
K Embeddings: Learning Conceptual Embeddings for Words using Context
... assume different roles (syntax) or meanings (semantics) presents a basic challenge to the notion of word embedding (Erk and Pad´o, 2008; Reisinger and Mooney, 2010; Huang et ... See full document
6
Learning to Embed Words in Context for Syntactic Tasks
... improve syntactic analysis of Twitter. Qualitatively, our token embeddings are shown to encode sense and POS information, grouping together tokens of different types with similar ... See full document
11
WiC: the Word in Context Dataset for Evaluating Context Sensitive Meaning Representations
... multi-prototype embeddings 10 . JBT 11 (Pelevina et al., 2016) induces different senses by clustering graphs constructed using word embed- dings and computes embedding for each cluster ...jointly ... See full document
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context2vec: Learning Generic Context Embedding with Bidirectional LSTM
... Substitute vectors (Yuret, 2012) represent contexts as a probabilistic distribution over the potential gap-filler words for the target slot, pruned to its top-k most probable words. While using this rep- resentation ... See full document
11
Unit Segmentation of Argumentative Texts
... the context around each token, we an- alyze different semantic, syntactic, structural, and pragmatic feature types, and we compare three fun- damental machine learning techniques based ... See full document
11
Jointly optimizing word representations for lexical and sentential tasks with the C PHRASE model
... at different levels of ...ter word vectors, presumably because C-PHRASE is trained to predict how the contexts of a word change based on its phrasal collocates (cup will have very different ... See full document
11
k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific Text
... on learning embedding hyper-parameters is relatively short [6, ...their embeddings on gen- eral topic using Wikipedia ...and different window sizes aiming to study the impact of syntactic ... See full document
15
The Role of Context Types and Dimensionality in Learning Word Embeddings
... continuous representations of words, parts-of-speech and de- pendency ...with word embeddings, which were pre-trained on unlabeled data, yields improved ...our different types of ... See full document
11
Improving Word Representations via Global Context and Multiple Word Prototypes
... single-prototype embeddings to represent each con- text window, which can then be used by clustering to perform word sense discrimination (Sch¨utze, ...fixed-sized context windows of all occur- ... See full document
10
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 ...our embeddings in ... See full document
8
Joint Slot Filling and Intent Detection via Capsule Neural Networks
... three types of capsules: 1) WordCaps that learn context-aware word representations, 2) SlotCaps that categorize words by their slot types via dynamic routing, and construct a ... See full document
9
Querying Word Embeddings for Similarity and Relatedness
... cue word embedding, more overlap is observed between human responses and model ...cue word car are names of automobile models such as suv and ...cue word embedding is more populated with such tax- ... See full document
10
Robust Word Vectors: Context Informed Embeddings for Noisy Texts
... produce embeddings for out-of-vocabulary (OOV) ...character-level embeddings (Ling et ...a word. This property is ensured by the fact that each word is encoded as a bag of ...time, word ... See full document
10
Experiments on human incremental parsing
... We believe that the experiments described in this paper characterize certain general features of human text comprehension. It could be argued that in fact we studied a much narrower phenomenon: text comprehension in ... See full document
7
HHMM at SemEval 2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
... We built a negative one-hot encoding feature vector to represent the inbound dependencies of the word corresponding to the role. Thus, for each dependency of the given role (in case of a multi- word ... See full document
5
Learning Composition Models for Phrase Embeddings
... a word phrase pair are semantically simi- ...candidate word for a given phrase from candi- date ...candidate word will be treated as a negative ... See full document
16
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
... Gov2Vec can be applied to more fine-grained cat- egories than entire government branches. In this context, there are often relationships between word sources, e.g. Obama after Bush, that we can incor- ... See full document
6
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... with word embeddings is by Sugawara(Sugawara et ...on context word embeddings (CWE) are merged, and they are used for training a classifier and ...the word in the ... See full document
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