Semantic network analysis of abstract and concrete word associations

In recent years, a new interest for the use of graph-theory based networks has emerged within the field of cognitive science. This has played a key role in mining the large amount of data generated by word association norms. In the present work, we applied semantic network analyses to explore norms of French word associations for concrete and abstract concepts (Lakhzoum et al., 2021). Graph analyses have shown that the network exhibits high clustering coefficient, sparse density, and small average shortest path length for both the concrete and abstract networks. These characteristics are consistent with a small-world structure. Comparisons between local node statistics and global structural topology showed that abstract and concrete concepts present a similar local connectivity but different overall patterns of structural organisation with concrete concepts presenting an organisation in densely connected communities compared to abstract concepts. These patterns confirm previously acquired knowledge about the dichotomy of abstract and concrete concepts on a larger scale. To the best of our knowledge, this is the first attempt to confirm the generalisability of these properties to the French language and with an emphasis on abstract and concrete concepts.

Phonovisual Biases in Language: is the Lexicon Tied to the Visual World?

The present paper addresses the study of cross-linguistic and cross-modal iconicity within a deep learning framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a concrete word, encoded as a sequence of feature vectors, to the visual representation of its referent, expressed as an HCNN-transformed image. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a randomized baseline; we show that such an imaginative network is capable of extracting language-independent generalizations in the mapping from linguistic sounds to visual features, providing empirical support for the hypothesis of a universal sound-symbolic substrate underlying all languages.

Love and Language

It is widely believed that love cannot be limited to language and has a more natural affinity with silence than with speech. In this chapter it is argued that although there is no exact fit between love and language, love needs language (and not just ‘looks’) if it is to be humanly meaningful. Following Kierkegaard, it is proposed that love is a response to the love commandment, understood not as an abstract ought but as a concrete word of personal address in the mode of what has been called vocativity. In this perspective we see that love is grounded in the call and involves the kind of promise that language makes possible. The Christian marriage service is read phenomenologically as revealing what the word of love requires, abstracted from its doctrinal content. The interrelationship of love and language is further explored with reference again to Dante.

Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation

This study carries out a systematic intrinsic evaluation of the semantic representations learned by state-of-the-art pre-trained multimodal Transformers. These representations are claimed to be task-agnostic and shown to help on many downstream language-and-vision tasks. However, the extent to which they align with human semantic intuitions remains unclear. We experiment with various models and obtain static word representations from the contextualized ones they learn. We then evaluate them against the semantic judgments provided by human speakers. In line with previous evidence, we observe a generalized advantage of multimodal representations over language- only ones on concrete word pairs, but not on abstract ones. On the one hand, this confirms the effectiveness of these models to align language and vision, which results in better semantic representations for concepts that are grounded in images. On the other hand, models are shown to follow different representation learning patterns, which sheds some light on how and when they perform multimodal integration.

Does visualisation help or hinder concrete word processing?

In an iconicity judgement task, participants were asked whether word pairs were iconic (e.g., nose–tongue; joy–sorrow) or reverse-iconic (e.g., tongue–nose; sorrow–joy), and an advantage for abstract word pairs (i.e., joy–sorrow) was found. Malhi and Buchanan proposed that this reverse concreteness, or abstractness, effect was the result of participants taking a visualisation/imagining (time-costly) approach towards the concrete word pairs and an emotional/intuitive (time-efficient) approach towards the abstract word pairs. This study tested this proposal by asking participants questions about strategy use (Experiment 1). In the forced-choice questions, all participants reported using a visualisation/imagining approach towards the concrete word pairs and most participants reported using an emotional/intuitive approach towards the abstract word pairs. In the open-ended responses, visual-spatial reasoning and real-life experience emerged as themes for the concrete word pairs and social norms and values emerged as themes for the abstract word pairs, adding to our understanding of the grounding of abstract words. In Experiment 2, participants were supplied with pictures as an aid to visualisation with the expectation that this would reduce the time required for concrete word processing. Supplying pictures made participants faster and more accurate at completing the task. Experiment 3 manipulated the type of visual aid by also supplying pictures that did not match the orientation of the word pairs. Participants were only more accurate when the pictures were in the correct and iconic spatial arrangement. A flexible abstractness and concreteness effects (FACE) theory is proposed which integrates symbolic and embodied accounts and introduces constructs such as direct and constrained imageability for concrete words and indirect and free imageability for abstract words.

Abstract Semantic Associative Network Training: A Replication and Update of an Abstract Word Retrieval Therapy Program

Purpose We report on a study that replicates previous treatment studies using Abstract Semantic Associative Network Training (AbSANT), which was developed to help persons with aphasia improve their ability to retrieve abstract words, as well as thematically related concrete words. We hypothesized that previous results would be replicated; that is, when abstract words are trained using this protocol, improvement would be observed for both abstract and concrete words in the same context-category, but when concrete words are trained, no improvement for abstract words would be observed. We then frame the results of this study with the results of previous studies that used AbSANT to provide better evidence for the utility of this therapeutic technique. We also discuss proposed mechanisms of AbSANT. Method Four persons with aphasia completed one phase of concrete word training and one phase of abstract word training using the AbSANT protocol. Effect sizes were calculated for each word type for each phase. Effect sizes for this study are compared with the effect sizes from previous studies. Results As predicted, training abstract words resulted in both direct training and generalization effects, whereas training concrete words resulted in only direct training effects. The reported results are consistent across studies. Furthermore, when the data are compared across studies, there is a distinct pattern of the added benefit of training abstract words using AbSANT. Conclusion Treatment for word retrieval in aphasia is most often aimed at concrete words, despite the usefulness and pervasiveness of abstract words in everyday conversation. We show the utility of AbSANT as a means of improving not only abstract word retrieval but also concrete word retrieval and hope this evidence will help foster its application in clinical practice.

An Analysis on William Ernest Henley’s Invictus

The research is an analysis the poem structure of William Ernest Henley Invictus. The purpose of this research is to understand the structure that used in thepoem of William Ernest Henley Invictus. The data were taken fromdocumentation of Invictus poem and biographical of the poet. Theresearcher used objective approach as data analysis method. The structureof poetry is consisting of two structures, namely; physical structure and innerstructure. The physical structure is consisting of six sections, they are diction,imagery, concrete word, figurative language, version and typography. While, the inner structure is divide on four proportion they are theme, tone, feelingand mandate. To make a good poetry the poet should pay attention in theelements of poetry, especially the structure of poetry.

Semiotic spaces in antidiscriminatory political discourse: Naming practices as indexes

ABSTRACTThis article explores the semiotic spaces occupied by organizations working against discrimination in Sweden. Expressions of identity, norm critique, and political goals are studied in relation to word production and language policy and planning. The study departs from interviews with representatives from three organizations within the hbtqi, antiracist, and disability movements. Other resources connected to them have also been analyzed, such as glossaries. Theoretically, this study draws on Yuri Lotman's concept of semiospheres, allowing the analysis to weigh in the whole semiotic process, including meaning production, policy work, and concrete word production. This approach completes an analysis of indexical orders. The results show that (a) organizations are aware of the importance of linguistic choices, (b) when new concepts and words are spread to the public, tension can arise and sometimes objections, and (c) word meanings change when used in public discourse. (Language policy and planning, semiosphere, indexical order, hbtqi, antiracism, disability, discrimination)

Word Sense Disambiguation Studio: A Flexible System for WSD Feature Extraction

The paper presents a flexible system for extracting features and creating training and testexamples for solving the all-words sense disambiguation (WSD) task. The system allowsintegrating word and sense embeddings as part of an example description. The system possessestwo unique features distinguishing it from all similar WSD systems—the ability to construct aspecial compressed representation for word embeddings and the ability to construct training andtest sets of examples with different data granularity. The first feature allows generation of data setswith quite small dimensionality, which can be used for training highly accurate classifiers ofdifferent types. The second feature allows generating sets of examples that can be used for trainingclassifiers specialized in disambiguating a concrete word, words belonging to the samepart-of-speech (POS) category or all open class words. Intensive experimentation has shown thatclassifiers trained on examples created by the system outperform the standard baselines formeasuring the behaviour of all-words WSD classifiers.