LADEC: The Large Database of English Compounds

Evolution of the Moral Lexicon

Morality is central to social well-being and cognition, and moral lexicon is a key device for human communication of moral concepts and experiences. How was the moral lexicon formed? We explore this open question and hypothesize that words evolved to take on abstract moral meanings from concrete and grounded experiences. We test this hypothesis by analyzing semantic change and formation of over 800 words from the English Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. Across historical text corpora and dictionaries, we discover concrete-to-abstract shifts as words acquire moral meaning, in contrast with the broad observation that words become more concrete over time. Furthermore, we find that compound moral words tend to be derived from a concrete-to-abstract shift from their constituents, and this derivational property is more prominent in moral words compared to alternative compound words when word frequency is controlled for. We suggest that evolution of the moral lexicon depends on systematic metaphorical mappings from concrete domains to the moral domain. Our results provide large-scale evidence for the role of metaphor in shaping the historical development of the English moral lexicon.

Influence of the constituent morpheme boundary on compound word access

Embedded morphemes are thought to become available during the processing of multi-morphemic words, and impact access to the whole word. According to the edge-aligned embedded word activation theory Grainger & Beyersmann, (2017), embedded morphemes receive activation when the whole word can be decomposed into constituent morphemes. Thus, interfering with morphological decomposition also interferes with access to the embedded morphemes. Numerous studies have examined the effects of interfering with boundary and constituent-internal letters on morphological decomposition by comparing the effect of transposing letters at the morphemic boundary to constituent-internal letters. These studies, which report inconsistent findings, have typically used derived multi-morphemic words (e.g., cleaner), and sometimes use a control replacement letter condition that is not matched to the transposed letter conditions in terms of location. Across five experiments, we test the edge-aligned activation theory by examining the effects of replacing and transposing boundary and constituent-internal letters of compounds. Our findings suggest that replacing boundary letters interferes with access to both embedded constituents, while replacing constituent-internal letters still allows for access to the unaltered constituent, thus compensating for the interference in the altered constituent. Our findings are consistent with the edge-aligned theory with respect to letter replacement, and also imply that letter replacement must match the position of letter transposition when it is used as a control condition.

Connective Comprehension in Adults: The Influence of Lexical Transparency, Frequency, and Individual Differences

The comprehension of connectives is crucial for understanding the discourse relations that make up a text. We studied connective comprehension in English to investigate whether adult comprehenders acquire the meaning and intended use of connectives to a similar extent and how connective features and individual differences impact connective comprehension. A coherence judgment study indicated that differences in how well people comprehend connectives depend on the lexical transparency but not on the frequency of the connective. Furthermore, individual variation between participants can be explained by their vocabulary size, nonverbal IQ, and cognitive reasoning style. Print exposure was not found to be relevant. These findings provide further insight into the factors that influence discourse processing and highlight the need to consider individual differences in discourse comprehension research as well as the need to examine a wider range of connectives in empirical studies of discourse markers.

LaDEP: A large database of English pseudo-compounds

The Large Database of English Pseudo-compounds (LaDEP) contains nearly 7500 English words which mimic, but do not truly possess, a compound morphemic structure. These pseudo-compounds can be parsed into two free morpheme constituents (e.g., car-pet), but neither constituent functions as a morpheme within the overall word structure. The items were manually coded as pseudo-compounds, further coded for features related to their morphological structure (e.g., presence of multiple affixes, as in ruler-ship), and summarized using common psycholinguistic variables (e.g., length, frequency). This paper also presents an example analysis comparing the lexical decision response times between compound words, pseudo-compound words, and monomorphemic words. Pseudo-compounds and monomorphemic words did not differ in response time, and both groups had slower response times than compound words. This analysis replicates the facilitatory effect of compound constituents during lexical processing, and demonstrates the need to emphasize the pseudo-constituent structure of pseudo-compounds to parse their effects. Further applications of LaDEP include both psycholinguistic studies investigating the nature of human word processing or production and educational or clinical settings evaluating the impact of linguistic features on language learning and impairments. Overall, the items within LaDEP provide a varied and representative sample of the population of English pseudo-compounds which may be used to facilitate further research related to morphological decomposition, lexical access, meaning construction, orthographical influences, and much more.

Conceptual combination during novel and existing compound word reading in context: A self-paced reading study

According to the relation-interpretation-competition-evaluation (RICE) hypothesis, compound word processing involves selecting a relational meaning (e.g., moonlight is 'light from moon') from a larger set of competing possible relational meanings. Prior lexical decision experiments with existing compound words have demonstrated that greater entropy of conceptual relations, i.e., greater competition between conceptual relations, impedes lexical processing speed. The present study addresses two unresolved issues: First, it is unclear whether the competition effect generalizes to the processing of novel compounds (e.g., grassladder), and second, it is not yet known whether competition between possible relational meanings extends to compounds when they are read in a sentence context. A series of self-paced reading tasks examined whether the competition effect operates regardless of (i) compound type (existing vs. novel), and (ii) whether sentence context (semantically supportive vs. semantically non-supportive) moderates the competition effect. The experiments confirmed that reading times of novel and existing compounds read in sentences were impacted by entropy of conceptual relations. Moreover, the effect was equally strong in both sentence context types. Additional analyses indicated that relational meanings are more ambiguous and flexible across different contexts for novel compounds compared to existing compounds.

Intelligent Robot English Speech Recognition Method Based on Online Database

In order to solve the problem of low accuracy of traditional English speech recognition, an intelligent robot English speech recognition method based on online database is proposed. A speech recognition device is installed on the intelligent robot as the hardware support for running the speech recognition method. The online English speech standard database is constructed to provide reference data for speech recognition. The real-time speech information is collected, and the speech signal is preprocessed by pre-emphasis, framing, windowing and other steps. According to the principle of speech signal generation, the features of speech signal are extracted, and the results of English speech recognition are obtained by similarity calculation and matching. Compared with the traditional recognition method, the experimental results show that the recognition rate of the optimised speech recognition method is improved by 1.3%, i.e. the recognition accuracy is improved.

Patterns in CAOSS: Distributed representations predict variation in relational interpretations for familiar and novel compound words

While distributional semantic models that represent word meanings as high-dimensional vectors induced from large text corpora have been shown to successfully predict human behavior across a wide range of tasks, they have also received criticism from different directions. These include concerns over their interpretability (how can numbers specifying abstract, latent dimensions represent meaning?) and their ability to capture variation in meaning (how can a single vector representation capture multiple different interpretations for the same expression?). Here, we demonstrate that semantic vectors can indeed rise up to these challenges, by training a mapping system (a simple linear regression) that predicts inter-individual variation in relational interpretations for compounds such as wood brush (for example brush FOR wood, or brush MADE OF wood) from (compositional) semantic vectors representing the meanings of these compounds. These predictions consistently beat different random baselines, both for familiar compounds (moon light, Experiment 1) as well as novel compounds (wood brush, Experiment 2), demonstrating that distributional semantic vectors encode variations in qualitative interpretations that can be decoded using techniques as simple as linear regression.

Psycholinguistic norms for a set of 506 French compound words

Compounds are morphologically complex words made of different linguistic parts. They are very prevalent in a number of languages such as French. Different psycholinguistic characteristics of compounds have been used in certain studies to investigate the mechanisms involved in compound processing (see Table 7). We provide psycholinguistic norms for a set of 506 French compound words. The words were normed on seven characteristics: lexeme meaning dominance, semantic transparency, sensory experience, conceptual familiarity, imageability, age of acquisition (AoA) and subjective frequency. Reliability measures were computed for the collected norms. Descriptive statistical analyses, and correlational and multiple regression analyses were performed. We also report some comparisons made between our normative data and certain norms obtained in other similar studies. The entire set of norms, which will be very useful to researchers investigating the processing of compounds, is available as Supplemental Material.

The RK processor: A program for analysing metaphor and word feature-listing data

Feature-listing tasks are an invaluable resource for exploring how words, categories, and metaphors are represented. However, manually coding the generated features is time-consuming and expensive, and involves subjective judgments from the experimenter. The purpose of this paper is to introduce the "RK processor", a program that was developed in our lab to analyse metaphor feature data but which can also be applied to other feature-listing data. After detailing the steps of processing, we demonstrate that the processed feature data align with previous findings in which metaphor features were processed manually and that the processed features predict dimensions of metaphor judgments pertaining to comprehensibility and metaphor goodness. Lastly, we present several other applications for research on word similarity, compound words, categories and concepts, semantic ambiguity, incongruity resolution and computational modelling. The RK processor offers researchers a valuable tool to save time and resources and to maintain consistency in processing.

CompLex: an eye-movement database of compound word reading in English

The CompLex database presents a large-scale collection of eye-movement studies on English compound-word processing. A combined total of 440 participants completed eye-tracking experiments in which they silently read unspaced English compound words (e.g., goalpost) embedded in sentence contexts (e.g., Dylan hit the goalpost when he was aiming for the net.). Three studies were conducted using participants representing the non-college-bound population (300 participants), and four studies included participants recruited from the student population (140 participants). The database comprises trial-level eye-movement data (47,763 trials), participant data (including a measure of reading experience estimated via the Author Recognition Test), and lexical characteristics for the set of 931 English compound words used as critical stimuli in the studies. One contribution of the present paper is a set of regression analyses conducted on the full database and individual experiments. We report that the most reliable and consistent main effects were those elicited by compound word length, left constituent frequency, right constituent frequency, compound frequency and semantic transparency. Separately, we also found that the effect of left frequency and compound word length is weaker among more frequent compounds. Another contribution is a power analysis, in which we determined the sample sizes required to reliably detect effect sizes that are comparable to those observed in our regression models. These sample size estimates serve as a recommendation for researchers wishing to either collect eye-movement data for compound word reading, or use the current database as a resource for the study of English compound word processing.