Combining corpus linguistic and psychological data on word co-occurrences: Corpus collocates versus word associations

Mollin, Sandra

doi:10.1515/cllt.2009.008

Cited by 70 publications

(48 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In other words, do we produce a negative response to a negative cue because of their congruency in valence, or because they have similar (negative) meanings? It often is assumed that semantic similarity is the strongest determinant of response tendencies (Mollin, 2009), yet this does not necessarily rule out any influence of the psycho-affective properties of a word: these properties could correspond to semantic features, in which case the likelihood that the response depends on similarity to the cue would increase.…”

Section: Discussionmentioning

confidence: 99%

Examining assortativity in the mental lexicon: Evidence from word associations

2015

View full text Add to dashboard Cite

Words are characterized by a variety of lexical and psychological properties, such as their part of speech, wordfrequency, concreteness, or affectivity. In this study, we examine how these properties relate to a word's connectivity in the mental lexicon, the structure containing a person's knowledge of words. In particular, we examine the extent to which these properties display assortative mixing, that is, the extent to which words in the lexicon are more likely to be connected to words that share these properties. We investigated three types of word properties: 1) subjective word covariates: valence, dominance, arousal, and concreteness; 2) lexical information: part of speech; and 3) distributional word properties: age-of-acquisition, word frequency, and contextual diversity. We assessed which of these factors exhibit assortativity using a word association task, where the probability of producing a certain response to a cue is a measure of the associative strength between the cue and response in the mental lexicon. Our results show that the extent to which these aspects exhibit assortativity varies considerably, with a high cue-response correspondence on valence, dominance, arousal, concreteness, and part of speech, indicating that these factors correspond to the words people deem as related. In contrast, we find that cues and responses show only little correspondence on word frequency, contextual diversity, and age-of-acquisition, indicating that, compared to subjective and lexical word covariates, distributional properties exhibit only little assortativity in the mental lexicon. Possible theoretical accounts and implications of these findings are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Examining assortativity in the mental lexicon: Evidence from word associations

2015

View full text Add to dashboard Cite

show abstract

“…Lexical centrality Associative knowledge is a central component in many accounts of recall, recognition, and semantic representations in word processing. There are multiple ways to tap into this knowledge, but word associations are considered to be the most direct route for gaining insight into our semantic knowledge (Nelson, McEvoy, & Schreiber, 2004;Mollin, 2009) and human thought in general (Deese, 1965). The type of information produced by word associations is capable of expressing any kind of semantic relationship between words.…”

mentioning

confidence: 99%

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

2012

View full text Add to dashboard Cite

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multipleresponse free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do singleresponse procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.Keywords Word associations . Semantic network . Lexical decision . Semantic relatedness . Lexical centrality Associative knowledge is a central component in many accounts of recall, recognition, and semantic representations in word processing. There are multiple ways to tap into this knowledge, but word associations are considered to be the most direct route for gaining insight into our semantic knowledge (Nelson, McEvoy, & Schreiber, 2004;Mollin, 2009) and human thought in general (Deese, 1965). The type of information produced by word associations is capable of expressing any kind of semantic relationship between words. Because of this flexibility, networks are considered the natural representation of word associations, where nodes correspond to lexicalized concepts and links indicate semantic or lexical relationships between two nodes. These networks correspond to an idealized localist representation of our mental lexical network. The properties derived from such a network have been instrumental in three different research traditions, which will be described below. These traditions have focused on (1) direct association strength, (2) second-order strength and distributional similarity, and (3) network topology and centrality measures.The first tradition has used word associations to calculate a measure of associative strength and was inspired by a behaviorist view of language in terms of stimulus-response patterns. This notion of associative strength plays an important role in studies that have focused on inhibition and facilitation in list learning (e.g., Roediger & Neely, 1982), studies on episodic memory (e.g., Nelson et al., 2004), and studies that have tried to distinguish semantic and associative priming (for a recent over...

show abstract

“…Within this field, word associations are also considered a useful tool to reveal information about the organization of the lexicon (Mollin 2009, Fitzpatrick et al 2011) and the lexical retrieval process (Nordquist 2009). The term 'word association' originated in psychology to refer to the word or words that first come to mind in response to a subject being presented with a stimulus word.…”

Section: Background On Similaritymentioning

confidence: 99%

“…Several comparisons between corpus data and word associations have been carried out previously (Mollin 2009;Nordquist 2009), but they have been mostly based on collocational information, aiming to test the psycholinguistic reality or relevance of collocations extracted from corpora. Indeed, from a usage-based perspective, the hypothesis that conventionalized linguistic structures should appear both in corpora and elicited data is a compelling one (Nordquist 2009).…”

Section: Introductionmentioning

confidence: 99%

Verb similarity: Comparing corpus and psycholinguistic data

Gil-Vallejo

Coll-Florit

Masalles

et al. 2018

Corpus Linguistics and Linguistic Theory

View full text Add to dashboard Cite

Similarity, which plays a key role in fields like cognitive science, psycholinguistics and natural language processing, is a broad and multifaceted concept. In this work we analyse how two approaches that belong to different perspectives, the corpus view and the psycholinguistic view, articulate similarity between verb senses in Spanish. Specifically, we compare the similarity between verb senses based on their argument structure, which is captured through semantic roles, with their similarity defined by word associations. We address the question of whether verb argument structure, which reflects the expression of the events, and word associations, which are related to the speakers' organization of the mental lexicon, shape similarity between verbs in a congruent manner, a topic which has not been explored previously. While we find significant correlations between verb sense similarities obtained from these two approaches, our findings also highlight some discrepancies between them and the importance of the degree of abstraction of the corpus annotation and psycholinguistic representations.

show abstract

Combining corpus linguistic and psychological data on word co-occurrences: Corpus collocates versus word associations

Cited by 70 publications

References 7 publications

Examining assortativity in the mental lexicon: Evidence from word associations

Examining assortativity in the mental lexicon: Evidence from word associations

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

Verb similarity: Comparing corpus and psycholinguistic data

Contact Info

Product

Resources

About