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Found 12 papers, 0 papers with code
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Given a set of candidate entities (e. g. movie titles), the ability to identify similar entities is a core capability of many recommender systems.
A Comparison of Context-sensitive Models for Lexical Substitution
Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks.
Paraphrase-Sense-Tagged Sentences
Many natural language processing tasks require discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a challenge.
Learning Scalar Adjective Intensity from Paraphrases
Adjectives like {``}warm{''}, {``}hot{''}, and {``}scalding{''} all describe temperature but differ in intensity.
Comparing Constraints for Taxonomic Organization
In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy.
Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation
We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions.
KnowYourNyms? A Game of Semantic Relationships
Semantic relation knowledge is crucial for natural language understanding.
Mapping the Paraphrase Database to WordNet
WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage.
Word Sense Filtering Improves Embedding-Based Lexical Substitution
The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense.
The Language of Place: Semantic Value from Geospatial Context
There is a relationship between what we say and where we say it.
