Multi-Prototype Vector-Space Models of Word Meaning

By Joseph Reisinger, Raymond J. Mooney. 2010

425 citations recorded by the source corpus.

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

In Representation & Self-Supervised Learning

Citations in the corpus

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018, background): “To get around this limitation dozens of proposals have been put forward, mainly in two categories: multi-prototype embeddings (Reisinger and Mooney, 2010; Neelakantan et al., 2014; Pelevina et al., 2016), which usually leverage context clustering in order to learn distinct representations for…”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, background): “Much recent research in distributional semantics does not distinguish between association and similarity in a principled way (see e.g. (Huang et al., 2012; Reisinger and Mooney, 2010b; Luong et al., 2013)).”
Multi-Relational Latent Semantic Analysis (2013, background): “In the context of lexical semantics, VSMs provide a natural way of measuring semantic word relatedness by computing the distance between the corresponding vectors, which has been a standard approach (Agirre et al., 2009; Reisinger and Mooney, 2010; Yih and Qazvinian, 2012).”
Multimodal Distributional Semantics (2013, background): “, along the lines of what was done for textual models by Reisinger and Mooney (2010).”
Polarity Inducing Latent Semantic Analysis (2012, methodology): “At the word level, vector representations have been used to measure word similarity (Deerwester et al., 1990; Turney and Littman, 2005; Turney, 2006; Turney, 2001; Lin, 1998; Agirre et al., 2009; Reisinger and Mooney, 2010) and for language modeling (Bellegarda, 2000; Coccaro and Jurafsky, 1998).”
Combining Heterogeneous Models for Measuring Relational Similarity (2013, background): “…similarity , which measures whether two words share similar attributes and is addressed in extensive research work (Bu-danitsky and Hirst, 2006; Reisinger and Mooney, 2010; Radinsky et al., 2011; Agirre et al., 2009; Yih and Qazvinian, 2012), measuring relational similarity is a relatively new…”
De-Conflated Semantic Representations (2016, methodology): “Though, these techniques usually assume a ﬁxed number of word senses per word type, disregarding the fact that the number of senses of a word can range from one (monosemy) to dozens.”
Prototype-Incorporated Emotional Neural Network (2018, background): “In other related works, chorus of prototypes and multiprototypes were proposed for more robust and meaningful learning [40]–[45].”
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model (2019, methodology): “DSM s have also been computed for linguistic items other than words, e.g., word senses—based both on meaning inventories (Rothe and Sch¨utze, 2015) and word sense induction techniques (Bartunov et al., 2015)—or meaning exemplars (Reisinger and Mooney, 2010; Erk and Pad´o, 2010; Reddy et al., 2011).”
Embeddings for Word Sense Disambiguation: An Evaluation Study (2016, background): “Several research studies have investigated the representation of word senses, instead of words (Reisinger and Mooney, 2010; Huang et al., 2012; Camacho-Collados et al., 2015b; Iacobacci et al., 2015; Rothe and Sch¨utze, 2015).”
Topical Word Embeddings (2015, methodology): “In experiments, we compare our models with baselines including C&W model (Collobert and Weston 2008), TFIDF (including Pruned TFIDF and Pruned TFIDF-M) (Reisinger and Mooney 2010), Huang’s model (Huang et al.”
Improving Word Representations via Global Context and Multiple Word Prototypes (2012, background): “Pruned tf-idf (Reisinger and Mooney, 2010b) and ESA (Gabrilovich and Markovitch, 2007) are also included.”
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection (2017, background): “One line of work (Dinu and Lapata, 2010; Reisinger and Mooney, 2010) views each word as a set of latent word senses.”
Gaussian LDA for Topic Models with Word Embeddings (2015, background): “An interesting extension to our work would be the ability to handle polysemous words based on multi-prototype vector space models (Neelakantan et al., 2014; Reisinger and Mooney, 2010) and we keep this as an avenue for future research.”
Hash Embeddings for Efficient Word Representations (2017, background): “Reisinger and Mooney (2010) and since then Huang et al. (2012) have used multiple different word embeddings (prototypes) for the same words for representing different possible meanings of the same words.”