Placing search in context: the concept revisited

By L. Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, G. Wolfman, Eytan Ruppin. 2002

1,875 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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Citations in the corpus

GloVe: Global Vectors for Word Representation (2014, background): “These include WordSim-353 (Finkelstein et al., 2001), MC (Miller and Charles, 1991), RG (Rubenstein and Goodenough, 1965), SCWS (Huang et al., 2012), and RW (Luong et al., 2013).”
AI and the Everything in the Whole Wide World Benchmark (2021, background): “of natural language processing, most notably inspiring peer databases but also direct derivative datasets such as WordSim-353 [Finkelstein et al., 2001] and now over 200 language translations.”
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016, background): “Table 1: The columns show the performance of the original w2vNEWS embedding (“before”) and the debiased w2vNEWS on the standard evaluation metrics measuring coherence and analogy-solving abilities: RG [32], WS [12], MSR-analogy [26].”
Analysis Methods in Neural Language Processing: A Survey (2018, background): “Datasets containing such similarity scores are often used to evaluate word embeddings (Finkelstein et al., 2002; Bruni et al., 2012; Hill et al., 2015, inter alia) or sentence embeddings; see the many shared tasks on semantic textual similarity in Se-mEval (Cer et al., 2017, and previous editions).”
Learning Gender-Neutral Word Embeddings (2018, methodology): “Embeddings are tested on multiple datasets: WS353-ALL (Finkel-stein et al., 2001), RG-65 (Rubenstein and Goode-nough, 1965), MTurk-287 (Radinsky et al., 2011), MTurk-771 (Halawi et al., 2012), RW (Luong et al., 2013), and MEN-TR-3k (Bruni et al., datasets.”
Towards Universal Paraphrastic Sentence Embeddings (2015, methodology): “They give human-level performance on two commonly used word similarity datasets, WordSim353 (Finkelstein et al., 2001) and Simlex-999 (Hill et al.”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, methodology): “Nevertheless, in what is currently the most popular evaluation gold standard for semantic similarity, WordSim(WS)-353 (Finkelstein et al., 2001), coffee and cup are rated as more ‘similar’ than pairs such as car and train, which share numerous common properties (function, material, dynamic behaviour”
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing (2020, methodology): “Intrinsic evaluation We picked two benchmark datasets to evaluate semantic relationships between word pairs: WordSimilarity-353 [12], consisting of similatiry judgments for 353 word pairs on a scale of 0 to 10.”
Bayesian Neural Word Embedding (2016, methodology): “The algorithms are evaluated on two different tasks: the word similarity task [16] and the word analogy task [4].”
Word Representations via Gaussian Embedding (2014, methodology): “We evaluate the embeddings on seven different standard word similarity benchmarks (Rubenstein & Goodenough, 1965; Szumlanski et al., 2013; Hill et al., 2014; Miller & Charles, 1991; Bruni et al., 2014; Yang & Powers, 2006; Finkelstein et al., 2001).”
Improving Distributional Similarity with Lessons Learned from Word Embeddings (2015, methodology): “Word Similarity We used six datasets to evaluate word similarity: the popular WordSim353 (Finkelstein et al., 2002) partitioned into two datasets, WordSim Similarity and WordSim Relatedness (Zesch et al.”
Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014, methodology): “Finkelstein et al. (2002) introduced the widely used WordSim353 set (ws) that, as the name suggests, consists of 353 pairs.”
Charagram: Embedding Words and Sentences via Character n-grams (2016, methodology): “For word similarity, we focus on two of the most commonly used datasets for evaluating semantic similarity of word embeddings: WordSim-353 (WS353) (Finkelstein et al., 2001) and SimLex-999 (SL999) (Hill et al., 2015).”
Improving Vector Space Word Representations Using Multilingual Correlation (2014, background): “The ﬁrst one is the WS-353 dataset (Finkelstein et al., 2001) containing 353 pairs of English words that have been assigned similarity ratings by humans.”
Dependency-Based Word Embeddings (2014, methodology): “To that end, we use the WordSim353 dataset (Finkelstein et al., 2002; Agirre et al., 2009).”
What’s in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation (2015, positive): “from the WS353 corpus (Finkelstein et al., 2002).”
Compositional Morphology for Word Representations and Language Modelling (2014, neutral): “But also on the standard English WS353 dataset (Finkelstein et al., 2002), we get a 26% better correlation with the human ratings.”