A novel embedding approach to learn word vectors by weighting semantic relations: SemSpace

Orhan, Umut; Tulu, Cagatay Neftali

doi:10.1016/j.eswa.2021.115146

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The idea of deriving meaning representations in terms of vectors from semantic networks has been previously investigated in the computational linguistics literature (e.g. Chakaveh et al, 2018;Grover & Leskovec, 2016;Orhan & Tulu, 2021;Perozzi et al, 2014;Pilehvar & Navigli, 2015; for review, see Grohe, 2020). For instance, Perozzi et al (2014) obtained vectors for each node in a graph by applying the random walk method.…”

Section: Spreading Activation As a Theory Of Semantic Processingmentioning

confidence: 99%

Spatial versus graphical representation of distributional semantic knowledge.

Mao,

Huebner,

Willits

2024

Psychological Review

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Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences. We compare our model to existing spatial and graphical models by systematically varying parameters that contributing to dimensions of theoretical interest in semantic modeling. In order to be certain about what the models should be able to learn, we trained each model on an artificial corpus describing events in an artificial world simulation containing experimentally controlled verb-noun selectional preferences. The task used for model evaluation requires recovering observed selectional preferences and inferring semantically plausible but never observed verb-noun pairs. We show that the distributional graphical model performed better than all other models. Further, we argue that the relative success of this model comes from its improved ability to access the different orders of spatial representations with the spreading activation on the graph, enabling the model to infer the plausibility of noun-verb pairs unobserved in the training data. The model integrates classical ideas of representing semantic knowledge in a graph with spreading activation and more recent trends focused on the extraction of lexical distributional data from large natural language corpora.

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Section: Spreading Activation As a Theory Of Semantic Processingmentioning

confidence: 99%

Spatial versus graphical representation of distributional semantic knowledge.

Mao,

Huebner,

Willits

2024

Psychological Review

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“…Tulu et al [ 41 ] proposed an automatic scoring system based on SemSpace [ 42 ], a novel embedding approach to learn word vectors by weighting semantic relations, used to feed Manhattan LSTM network (which is precisely used for similarity tasks [ 43 ]). The proposed method has also been evaluated on CU-NLP dataset.…”

Section: Related Workmentioning

confidence: 99%

GradeAid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation

et al. 2023

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Automatic short answer grading (ASAG), a hot field of natural language understanding, is a research area within learning analytics. ASAG solutions are conceived to offload teachers and instructors, especially those in higher education, where classes with hundreds of students are the norm and the task of grading (short)answers to open-ended questionnaires becomes tougher. Their outcomes are precious both for the very grading and for providing students with “ad hoc” feedback. ASAG proposals have also enabled different intelligent tutoring systems. Over the years, a variety of ASAG solutions have been proposed, still there are a series of gaps in the literature that we fill in this paper. The present work proposes GradeAid, a framework for ASAG. It is based on the joint analysis of lexical and semantic features of the students’ answers through state-of-the-art regressors; differently from any other previous work, (i) it copes with non-English datasets, (ii) it has undergone a robust validation and benchmarking phase, and (iii) it has been tested on every dataset publicly available and on a new dataset (now available for researchers). GradeAid obtains performance comparable to the systems presented in the literature (root-mean-squared errors down to 0.25 based on the specific tuple $$\langle $$ ⟨ dataset-question$$\rangle $$ ⟩ ). We argue it represents a strong baseline for further developments in the field.

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Semantic similarity and mutual information-based model for fake news detection

Gorai,

Shaw

2024

Multimed Tools Appl

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A novel embedding approach to learn word vectors by weighting semantic relations: SemSpace

Cited by 10 publications

References 21 publications

Spatial versus graphical representation of distributional semantic knowledge.

Spatial versus graphical representation of distributional semantic knowledge.

GradeAid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation

Semantic similarity and mutual information-based model for fake news detection

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