54 research outputs found
Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
The notions of concreteness and imageability, traditionally important in
psycholinguistics, are gaining significance in semantic-oriented natural
language processing tasks. In this paper we investigate the predictability of
these two concepts via supervised learning, using word embeddings as
explanatory variables. We perform predictions both within and across languages
by exploiting collections of cross-lingual embeddings aligned to a single
vector space. We show that the notions of concreteness and imageability are
highly predictable both within and across languages, with a moderate loss of up
to 20% in correlation when predicting across languages. We further show that
the cross-lingual transfer via word embeddings is more efficient than the
simple transfer via bilingual dictionaries
āDuboka leksikografijaā ā pomodnost ili prilika?
In recent years, we are witnessing staggering improvements in various semantic data processing tasks due to the developments in the area of deep learning, ranging from image and video processing to speech processing, and natural language understanding. In this paper, we discuss the opportunities and challenges that these developments pose for the area of electronic lexicography. We primarily focus on the concept of representation learning of the basic elements of language, namely words, and the applicability of these word representations to lexicography. We first discuss well-known approaches to learning static representations of words, the so-called word embeddings, and their usage in lexicography-related tasks such as semantic shift detection, and cross-lingual prediction of lexical features such as concreteness and imageability. We wrap up the paper with the most recent developments in the area of word representation learning in form of learning dynamic, context-aware representations of words, showcasing some dynamic word embedding examples, and discussing improvements on lexicography-relevant tasks of word sense disambiguation and word sense induction.Posljednjih smo godina svjedoci velikoga napretka u razliÄitim zadatcima inteligentne obrade podataka koji je posljedica razvoja dubokoga uÄenja. ti zadatci ukljuÄuju i obradu slike, videa, govora te razumijevanje jezika. u ovome se radu raspravlja o prilikama i izazovima koje taj napredak omoguÄuje u podruÄju digitalne leksikografije.
VeÄi se dio rada odnosi na uÄenje prikaza razliÄitih elemenata jezika ā rijeÄi, leksema te izjava ā i njihovu primjenu u leksikografiji. Prikazuju se dobro poznati postupci uÄenja statiÄkih vektorskih prikaza rijeÄi te njihova primjena u zadatcima poput prepoznavanja semantiÄkih pomaka te predviÄanja leksiÄkih znaÄajka rijeÄi. U radu se dalje govori o viÅ”ejeziÄnoj razini uÄenja prikaza rijeÄi te se rad zakljuÄuje prikazom novijih postignuÄa u podruÄju strojnoga razumijevanja jezika ā dinamiÄkih, kontekstnih prikaza rijeÄi
Computational explorations of semantic cognition
Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the modelsā ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from āhealthyā models, and generate ālesionedā models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words
Learning the Visualness of Text Using Large Vision-Language Models
Visual text evokes an image in a person's mind, while non-visual text fails
to do so. A method to automatically detect visualness in text will enable
text-to-image retrieval and generation models to augment text with relevant
images. This is particularly challenging with long-form text as text-to-image
generation and retrieval models are often triggered for text that is designed
to be explicitly visual in nature, whereas long-form text could contain many
non-visual sentences. To this end, we curate a dataset of 3,620 English
sentences and their visualness scores provided by multiple human annotators. We
also propose a fine-tuning strategy that adapts large vision-language models
like CLIP by modifying the model's contrastive learning objective to map text
identified as non-visual to a common NULL image while matching visual text to
their corresponding images in the document. We evaluate the proposed approach
on its ability to (i) classify visual and non-visual text accurately, and (ii)
attend over words that are identified as visual in psycholinguistic studies.
Empirical evaluation indicates that our approach performs better than several
heuristics and baseline models for the proposed task. Furthermore, to highlight
the importance of modeling the visualness of text, we conduct qualitative
analyses of text-to-image generation systems like DALL-E. Project webpage:
https://gaurav22verma.github.io/text-visualness/Comment: Accepted at EMNLP 2023 (Main, long); 9 pages, 5 figure
Speaking while listening: Language processing in speech shadowing and translation
Contains fulltext :
233349.pdf (Publisherās version ) (Open Access)Radboud University, 25 mei 2021Promotores : Meyer, A.S., Roelofs, A.P.A.199 p
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding.
Yet, the majority of metaphor processing systems to date rely on hand engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task
A Psycholinguistic Analysis of BERT's Representations of Compounds
This work studies the semantic representations learned by BERT for compounds,
that is, expressions such as sunlight or bodyguard. We build on recent studies
that explore semantic information in Transformers at the word level and test
whether BERT aligns with human semantic intuitions when dealing with
expressions (e.g., sunlight) whose overall meaning depends -- to a various
extent -- on the semantics of the constituent words (sun, light). We leverage a
dataset that includes human judgments on two psycholinguistic measures of
compound semantic analysis: lexeme meaning dominance (LMD; quantifying the
weight of each constituent toward the compound meaning) and semantic
transparency (ST; evaluating the extent to which the compound meaning is
recoverable from the constituents' semantics). We show that BERT-based measures
moderately align with human intuitions, especially when using contextualized
representations, and that LMD is overall more predictable than ST. Contrary to
the results reported for 'standard' words, higher, more contextualized layers
are the best at representing compound meaning. These findings shed new light on
the abilities of BERT in dealing with fine-grained semantic phenomena.
Moreover, they can provide insights into how speakers represent compounds.Comment: To appear in the Proceedings of EACL 2023 (main conference
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