Distributional Semantics in Technicolor

By Elia Bruni, Gemma Boleda, Marco Baroni, Nam Khanh Tran. 2012

387 citations recorded by the source corpus.

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

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Citations in the corpus

Experience Grounds Language (2020, methodology): “Most of these works provide resources such as data, code, simulators and methodology for evaluating the multimodal content of linguistic representations (Schlangen, 2019b; Silberer and Lapata, 2014; Bruni et al., 2012).”
Learning Visual Features from Large Weakly Supervised Data (2015, methodology): “We evaluate our word embeddings on two datasets that capture different types of semantic information: (1) a syntactic-semantic questions dataset [31] and (2) the MEN word similarity dataset [4].”
Zero-Shot Learning Through Cross-Modal Transfer (2013, background): “[5] worked on perceptually grounding word meaning and showed that joint models are better able to predict the color of concrete objects.”
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, methodology): “tic changes over time). 3.1 Synchronic Accuracy We evaluated the synchronic (within-time-period) accuracy of the methods using a standard modern benchmark and the 1990s portion of the ENGALL data. On Bruni et al. (2012)’s MEN similarity task of matching human judgments of word similarities, SVD perf”
Microsoft COCO Captions: Data Collection and Evaluation Server (2015, background): “The automatic generation of captions for images is a long standing and challenging problem in artiﬁcial intelligence [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].”
Language Is Not All You Need: Aligning Perception with Language Models (2023, methodology): “To investigate the visual commonsense capabilities, we compare the zero-shot performance of K OSMOS -1 and LLM on three object commonsense reasoning datasets, R ELATIVE S IZE [36], M EMORY C OLOR [37] and C OLOR T ERMS [38] datasets.”
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, neutral): “, 2013), and MEN-TR-3k (Bruni et al., 2012)”
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech (2018, positive): “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].”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, methodology): “Further, 7 http://clic.cimec.unitn.it/ elia.bruni/MEN.html there is a strong bias towards concrete concepts in MEN because the concepts were originally selected from those identiﬁed in an image-bank (Bruni et al., 2012a).”
Multimodal Distributional Semantics (2013, methodology): “The simple approach of Bruni et al. (2011), in which the two matrices are concatenated without mixing, is the parametrization k=r, FL, α=0.”
Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017, neutral): “The majority of the NLP work in this space has focused on the downstream task of augmenting word representations with novel grounded information, often evaluating on standard semantic similarity datasets (Agirre et al., 2009; Bruni et al., 2012; Faruqui et al., 2015; Bulat et al., 2016).”
Combining Language and Vision with a Multimodal Skip-gram Model (2015, background): “MDSMs outperform state-of-the-art text-based approaches, not only in tasks that directly require access to visual knowledge (Bruni et al., 2012), but also on general semantic benchmarks (Bruni et al., 2014; Silberer and Lapata, 2014).”
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. (2016, methodology): “Categories E01 and E10 2E06-08: https://en.wikipedia.org/wiki/List of animal names E02: http://www.infoplease.com/ipa/A0855611.html are based on the Google test, and category E09 - on the color dataset (Bruni et al., 2012).”
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics (2014, methodology): “We conjecture that the performance increase on WordSim353 is due to increased performance on the similarity subset of that dataset.”
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks (2016, background): “Such anomalies are also found in recently constructed datasets like MEN (Bruni et al., 2012).”
Formal Distributional Semantics: Introduction to the Special Issue (2016, methodology): “Recently, researchers have started exploring other modalities, using, for instance, visual and auditory information extracted from images and sound ﬁles (Feng and Lapata 2010; Bruni et al. 2012; Roller and Schulte Im Walde 2013; Kiela and Clark 2015; Lopopolo and van Miltenburg 2015).”