2,780 research outputs found
Towards cognitively plausible data science in language research
Over the past 10 years, Cognitive Linguistics has taken a Quantitative Turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of language emerges from exposure to usage
Seeing the advantage: Visually grounding word embeddings to better capture human semantic knowledge
Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings.Importantly, in both experiments we show that he grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge
Distributional semantic models capture word-level meaning that is useful in
many natural language processing tasks and have even been shown to capture
cognitive aspects of word meaning. The majority of these models are purely text
based, even though the human sensory experience is much richer. In this paper
we create visually grounded word embeddings by combining English text and
images and compare them to popular text-based methods, to see if visual
information allows our model to better capture cognitive aspects of word
meaning. Our analysis shows that visually grounded embedding similarities are
more predictive of the human reaction times in a large priming experiment than
the purely text-based embeddings. The visually grounded embeddings also
correlate well with human word similarity ratings. Importantly, in both
experiments we show that the grounded embeddings account for a unique portion
of explained variance, even when we include text-based embeddings trained on
huge corpora. This shows that visual grounding allows our model to capture
information that cannot be extracted using text as the only source of
information
The ERP response to the amount of information conveyed by words in sentences
Contains fulltext :
132194.pdf (publisher's version ) (Open Access)Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence’s hierarchical structure for generating expectations about the upcoming word.11 p
On staying grounded and avoiding Quixotic dead ends
The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing
Predicting and Explaining Human Semantic Search in a Cognitive Model
Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive plausibility of their implementation. Existing work has also
neglected to consider the constraints that the incremental process of language
acquisition must place on the structure of semantic memory. Here we present a
model that incrementally updates a semantic network, with limited computational
steps, and replicates many patterns found in human semantic fluency using a
simple random walk. We also perform thorough analyses showing that a
combination of both structural and semantic features are correlated with human
performance patterns.Comment: To appear in proceedings for CMCL 201
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