Search

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text-based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.

In this paper, we introduce an interactive guide plate system by adopting a gaze-communicative stuffed-toy robot and a gaze-interactive display board. An attached stuffed-toy robot on the system naturally show anthropomorphic guidance corresponding to the user’s gaze orientation. The guidance is presented through gaze-communicative behaviors of the stuffed-toy robot using joint attention and eye-contact reactions to virtually express its own mind in conjunction with b) vocal guidance and c) projection on the guide plate. We adopted our image-based remote gaze-tracking method to detect the user’s gazing orientation. The results from both empirical studies by subjective / objective evaluations and observations of our demonstration experiments in a semipublic space show i) the total operation of the system, ii) the elicitation of user’s interest by gaze behaviors of the robot, and iii) the effectiveness of the gaze-communicative guide adopting the anthropomorphic robot.

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text‐based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected. [ABSTRACT FROM AUTHOR]

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. [ABSTRACT FROM AUTHOR]

This study examines the ability of a semantic space model to represent the meaning of noun compounds such as ‘information gathering’ or ‘heart disease.’ For a semantic space model to compute the meaning and the attributional similarity (or semantic relatedness) for unfamiliar noun compounds that do not occur in a corpus, the vector for a noun compound must be computed from the vectors of its constituent words using vector composition algorithms. Six composition algorithms (i.e., centroid, multiplication, circular convolution, predication, comparison, and dilation) are compared in terms of the quality of the computation of the attributional similarity for English and Japanese noun compounds. To evaluate the performance of the computation of the similarity, this study uses three tasks (i.e., related word ranking, similarity correlation, and semantic classification), and two types of semantic spaces (i.e., latent semantic analysis-based and positive pointwise mutual information-based spaces). The result of these tasks is that the dilation algorithm is generally most effective in computing the similarity of noun compounds, while the multiplication algorithm is best suited specifically for the positive pointwise mutual information-based space. In addition, the comparison algorithm works better for unfamiliar noun compounds that do not occur in the corpus. These findings indicate that in general a semantic space model, and in particular the dilation, multiplication, and comparison algorithms have sufficient ability to compute the attributional similarity for noun compounds. [ABSTRACT FROM PUBLISHER]

Previous metaphor studies have paid much attention to nominal metaphors and predicative metaphors, but little attention has been given to adjective metaphors. Although some studies have focused on adjective metaphors, they only examined differences in the acceptability of various types of adjective metaphors. This paper explores the cognitive effects evoked by adjective metaphors. Three psychological experiments revealed that (1) adjective metaphors, especially those modified by color adjectives, tend to evoke negative effect; (2) although the meanings of metaphors are basically affected by the meanings of their vehicles, when a vehicle has a neutral meaning, negative meanings are evoked most frequently for adjective metaphors compared to nominal and predicative metaphors; (3) negative meanings evoked by adjective metaphors are related to poeticness, and poetic metaphors evoke negative meanings more easily than less poetic metaphors. Our research sheds new light on studies of the use of metaphor, which is one of the most basic human cognitive abilities. [ABSTRACT FROM AUTHOR]

This study examined the relationship between reading literary novels and generating predictive inferences by analyzing a corpus of Japanese novels. Latent semantic analysis (LSA) was used to capture the statistical structure of the corpus. Then, the authors asked 74 Japanese college students to generate predictive inferences (e.g., "The newspaper burned") in response to Japanese event sentences (e.g., "A newspaper fell into a bonfire") and obtained more than 5,000 predicted events. The analysis showed a significant relationship between LSA similarity between the event sentences and the predicted events and frequency of the predicted events. This result suggests that exposure to literary works may help develop readers' inference generation skills. In addition, two vector operation methods for sentence vector constructions from word vectors were compared: the "Average" method and the "Predication Algorithm" method (Kintsch, 2001). The results support the superiority of the Predication Algorithm method over the Average method. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of how people understand predicative metaphors such as “The rumor flew through the office,” and argue that predicative metaphors are understood as indirect (or two-stage) categorizations. In the indirect categorization process, the verb (e.g., fly) of a predicative metaphor evokes an intermediate entity, which in turn evokes a metaphoric category of actions or states (e.g., “to spread rapidly and soon disappear”) to be attributed to the target noun (e.g., rumor), rather than directly evoking a metaphoric category as argued by Glucksberg's (2001) categorization theory. We test our argument using two experiments, namely, offline comprehension and online priming. The offline comprehension experiment showed that interpretation of predicative metaphors had greater overlap with words indirectly associated with the verb than those directly associated with the verb. The online priming experiment demonstrated that indirectly associated words were activated during predicative metaphor comprehension, but directly associated words were not. These results provide convergent evidence for our argument, and thus the psychological validity of two-stage categorization as a process of predicative metaphor comprehension was confirmed. [ABSTRACT FROM PUBLISHER]

Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view (), aptness view (), and interpretive diversity view (); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization and comparison processes. This article attempts to answer the question regarding which views are plausible by using cognitive modeling and computer simulation based on a semantic space model. In the simulation experiment, categorization and comparison processes are modeled in a semantic space constructed by latent semantic analysis. These two models receive word vectors for the constituent words of a metaphor and compute a vector for the metaphorical meaning. The resulting vectors can be evaluated according to the degree to which they mimic the human interpretation of the same metaphor; the maximum likelihood estimation determines which of the two models better explains the human interpretation. The result of the model selection is then predicted by three metaphor properties (i.e., vehicle conventionality, aptness, and interpretive diversity) to test the three views. The simulation experiment for Japanese metaphors demonstrates that both interpretive diversity and vehicle conventionality affect the choice between the two processes. On the other hand, it is found that metaphor aptness does not affect this choice. This result can be treated as computational evidence supporting the interpretive diversity and conventionality views. [ABSTRACT FROM AUTHOR]