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Found 4 papers, 2 papers with code
Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains
For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document.
Enhanced word embeddings using multi-semantic representation through lexical chains
The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually.
Multi-sense embeddings through a word sense disambiguation process
Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data.
Why Machines Cannot Learn Mathematics, Yet
Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods.
