| Challenge: | Existing theories for word classification and clustering are lacking. |
| Approach: | They propose a theory that uses a function to represent a word by its co-occurrences with other words in context. |
| Outcome: | The proposed model improves word classification and clustering by using multiple features. |
Similar Papers
Quantifying Context Overlap for Training Word Embeddings (D18-1)
Copied to clipboard
| Challenge: | Experimental results show that word embeddings can be improved using word embeds . word embedings are a popular form of natural language processing . |
| Approach: | They propose to estimate second order co-occurrence relations based on context overlap . they use the augmented data to enhance word embeddings learning . |
| Outcome: | The proposed model improves word vectors for word similarity and downstream NLP tasks. |
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (D18-1)
Copied to clipboard
| Challenge: | Existing approaches combine language and perception to infer word embeddings . however, the embeddables produced by such models do not reflect the actual word representations. |
| Approach: | They propose a probabilistic model that integrates linguistic and perceptual inputs to explain observed word-context pairs in a text corpus. |
| Outcome: | The proposed model achieves competitive or stronger results on tasks of assessing pairwise word similarity and image/caption retrieval compared to other state-of-the-art models. |
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to recognize lexical semantic relations between word pairs require that word pairs co-occur in a sentence. |
| Approach: | They propose to exploit lexico-syntactic paths between two target words to exploit the semantic relations between word pairs. |
| Outcome: | The proposed model can generalize the co-occurrences of word pairs and dependency paths and extract features capturing relational information from word pairs. |
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (P19-1)
Copied to clipboard
| Challenge: | Word embedding models typically learn two types of vectors: target word vectors and context word vector. |
| Approach: | They propose to explicitly impose a cluster structure on context word vectors to improve word embedding models. |
| Outcome: | The proposed model improves word embedding models qualitatively by imposing a cluster structure on the set of context word vectors. |
Unsupervised Learning of Distributional Relation Vectors (P18-1)
Copied to clipboard
| Challenge: | Existing word embedding models rely on co-occurrence statistics to learn vector representations of word meaning. |
| Approach: | They propose a method which directly learns relation vectors from co-occurrence statistics. |
| Outcome: | The proposed method is based on a variant of GloVe, which has an explicit connection between word vectors and PMI weighted co-occurrence vectors. |
Relation Induction in Word Embeddings Revisited (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
| Approach: | They propose to use Gaussian to explicitly model the variability of translations and Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words. |
| Outcome: | The proposed models are based on translations but use Gaussian to model the variability of translations and encode soft constraints on the source and target words that may be chosen. |
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (2020.lrec-1)
Copied to clipboard
Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
| Challenge: | Existing methods to evaluate word embeddings ignore context and treat words in isolation. |
| Approach: | They propose to build a new word embeddings-based dataset that provides context-dependent similarity measures. |
| Outcome: | The proposed dataset provides context-dependent similarity measures and covers a well-resourced language (English) but a number of less-resource languages. |
Revisiting the Context Window for Cross-lingual Word Embeddings (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embeddable spaces are structurally similar. |
| Approach: | They propose to use different context windows to evaluate bilingual word embeddings in various languages, domains, and tasks. |
| Outcome: | The size of both the source and target window improves bilingual lexicon induction, especially on frequent nouns. |
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers (P18-2)
Copied to clipboard
| Challenge: | cloze deletion test is a test that requires the learner to understand the context and vocabulary in order to identify the correct word. |
| Approach: | They collect data from human participants and test various models in a local and a global context condition to examine the role of linguistic context in predicting quantifiers. |
| Outcome: | The proposed models outperform humans in a local and global context and are only slightly better in the latter. |
Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences (N19-1)
Copied to clipboard
| Challenge: | Existing word embedding algorithms make a strong assumption that words are semantically related only if they co-occur locally within a window of fixed size. |
| Approach: | They propose a graph-based word embedding method that relies on locality to capture the semantic association between words that co-occur frequently but non-locally within documents. |
| Outcome: | The proposed method outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization. |