Enriching Word Embeddings with Temporal and Spatial Information
The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time- and location-stamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.
Code (1)
Tasks
Word EmbeddingsSimilar Papers 제목 키워드 기반
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View
Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings. In this paper, we argue that Chinese word embeddings can be substantially enriched by the morphological infor…
Word EmbeddingsWord SimilarityEnriching Word Sense Embeddings with Translational Context
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension
Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines. Previous methods tackle this problem by en…
Knowledge Graph EmbeddingsKnowledge GraphsMachine Reading ComprehensionReading ComprehensionTraining Word Sense Embeddings With Lexicon-based Regularization
We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm{'}s objective function through a reg…
Word EmbeddingsWord Sense DisambiguationConstrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings
In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem th…
Language ModelingLanguage ModellingWord EmbeddingsWord Similarity