A word at a time: computing word relatedness using temporal semantic analysis

By Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, Shaul Markovitch. The Web Conference, 2011

483 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aff0e7346ac75de0d1b2615e99e70d8b057f603d

How researchers see this paper

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

In Artificial Intelligence

Citations in the corpus

Learning Gender-Neutral Word Embeddings (2018, unknown): “, 2001), RG-65 (Rubenstein and Goodenough, 1965), MTurk-287 (Radinsky et al., 2011), MTurk-771 (Halawi et al.”
Conceptor Debiasing of Word Representations Evaluated on WEAT (2019, background): “The word similarity sets are: the RG65 (Rubenstein and Goodenough, 1965), the WordSim-353 (WS) (Finkelstein et al., 2002), the rare-words (RW) (Luong et al., 2013), the MEN dataset (Bruni et al., 2014), the MTurk (Radinsky et al., 2011), the SimLex-999 (SimLex) (Hill et al., 2015), and the SimVerb-3”
Combining Heterogeneous Models for Measuring Relational Similarity (2013, background): “…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 research direction…”
Question Answering Using Enhanced Lexical Semantic Models (2013, methodology): “Among various word similarity models (Agirre et al., 2009; Reisinger and Mooney, 2010; Gabrilovich and Markovitch, 2007; Radinsky et al., 2011), the vector space models (VSMs) based on the idea of distributional similarity (Turney and Pantel, 2010) are often used as the core component.”
Improving Vector Space Word Representations Using Multilingual Correlation (2014, methodology): “The fourth benchmark is the MTurk-287 (Radinsky et al., 2011) dataset that constitutes of 287 pairs of words and is different from the above two benchmarks in that it has been constructed by crowdsourcing the human similarity ratings using Amazon Mechanical Turk.”
All-but-the-Top: Simple and Effective Postprocessing for Word Representations (2017, methodology): “…of rarely used words; the MEN dataset (Bruni et al., 2014) where the 3000 pairs of words are rated by crowdsourced participants; the MTurk dataset (Radinsky et al., 2011) where the 287 pairs of words are rated in terms of relatedness; the SimLex-999 (SimLex) dataset (Hill et al., 2016) where the…”
Measuring Word Relatedness Using Heterogeneous Vector Space Models (2012, methodology): “More recently, Radinsky et al. (2011) analyze the temporal aspects of words and argue that non-identical terms in two term vectors should also be compared based on their temporal usage when computing the similarity score.”
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech (2018, unknown): “We used 13 benchmarks [30] to measure word similarity, including WS-353 [31], WS-353-REL [32], WS-353-SIM , MC-30 [33], RG-65 [34], Rare-Word [35], MEN [36], MTurk-287 [37], MTurk-771 [38], YP-130 [31], SimLex-999 [39], Verb-143 [40], and SimVerb-3500 [41].”
Metrology for AI: From Benchmarks to Instruments (2019, methodology): “8https://aclweb.org/aclwiki/WordSimilarity-353 Test Collection (State of the art) The authors measured the performance of CLEAR and CLEAR+TSA using WS353+ρ as the instrument, and compared to previously published approaches ESA and TSA (Radinsky et al. 2011) measured with the same instrument.”
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018, methodology): “…NLP tasks such as information extraction (Laender, Ribeiro-Neto, da Silva, & Teixeira, 2002), semantic role labeling (Erk, 2007), word similarity (Radinsky, Agichtein, Gabrilovich, & Markovitch, 2011), word sense disambiguation (Navigli, 2009) or spelling correction (Jones & Martin, 1997), inter…”
KORE: keyphrase overlap relatedness for entity disambiguation (2012, background): “[28] have exploited Wikipedia (and Zesch et al.”
Learning Word Embeddings from Speech (2017, unknown): “…, MC-30 [Miller and Charles, 1991], RG-65 [Rubenstein and Goodenough, 1965], Rare-Word [Luong et al., 2013], MEN [Bruni et al., 2012], MTurk-287 [Radinsky et al., 2011], MTurk-771 [Halawi et al., 2012], YP-130 [Yang and Powers, 2006], SimLex-999 [Hill et al., 2015], Verb-143 [Baker et al.,…”
Community Evaluation and Exchange of Word Vectors at wordvectors.org (2014, methodology): “The sixth benchmark is the MTurk-287 (Radinsky et al., 2011) dataset that constitutes 287 pairs of words and is different from the previous benchmarks in that it has been constructed by crowdsourcing the human similarity ratings using Amazon Mechanical Turk (AMT).”
Modeling and predicting behavioral dynamics on the web (2012, background): “Some studies [5, 16] used temporal patterns of queries to identify similar queries or words.”
Learning Word Representations with Hierarchical Sparse Coding (2014, unknown): “achine Learning Research ,12 , 22972334. [9] Kumar, A., Beutel, A., Ho, Q., and Xing, E. P. (2014). Fugue: Slow-worker-agnostic distributed learning for big models on big data. In Proc. of AISTATS . [10] Lee, H., Battle, A., Raina, R., and Ng, A. Y. (2007). Efcient sparse coding algorithms. In Proc.”