Two types of semantic similarity are usually distinguished: attributional and relational similarities. These similarities measure the degree between words or word pairs. Attributional similarities are bidrectional, while relational similarities are one-directional. It is possible to compute such similarities based on the occurrences of words in actual sentences. Inside sentences, syntagmatic as...

This paper presents the design and results of a crowdsourcing experiment on the recognition of Italian event nominals. The aim of the experiment was to assess the feasibility of crowdsourcing methods for a complex semantic task such as distinguishing the eventive interpretation of polysemous nominals taking into consideration various types of syntagmatic cues. Details on the theoretical backgro...

The syntagmatic paradigmatic model is a distributed, memory-based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long-term memory and the resolution of these constraints in working memory. Lexical informatio...

We investigate asymmetry in corpus-derived and human word associations. Most prior work has studied paradigmatic relations, either derived from free association norms or from large corpora using measures of statistical association and semantic relatedness. By contrast, we investigate the syntagmatic relation between words in adjective-noun and noun-noun combinations and present a new experiment...

This article presents SLAM, an Automatic Solver for Lexical Metaphors like “déshabiller* une pomme” (to undress* an apple). SLAM calculates a conventional solution for these productions. To carry on it, SLAM has to intersect the paradigmatic axis of the metaphorical verb “déshabiller*”, where “peler” (“to peel”) comes closer, with a syntagmatic axis that comes from a corpus where “peler une pom...

Most traditional distributional similarity models fail to capture syntagmatic patterns that group together multiple word features within the same joint context. In this work we introduce a novel generic distributional similarity scheme under which the power of probabilistic models can be leveraged to effectively model joint contexts. Based on this scheme, we implement a concrete model which uti...

Retrieving a word in a sentence requires speakers to overcome syntagmatic, as well as paradigmatic interference. When accessing cat in "The cat chased the string," not only are similar competitors such as dog and cap activated, but also other words in the planned sentence, such as chase and string. We hypothesize that both types of interference impact the same stage of lexical access, and revie...

This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically capturing semantic similarities between med...

This paper presents results of the first phase of our study aimed at investigating the applicability of word-space models, particularly those generated using Random Indexing (RI) technique, for the task of determining the relevance of messages posted in anchored asynchronous online discussion forums. In this phase, we addressed several questions intended to establish baseline figures: How effic...

Vector space representation of words has been widely used to capture fine-grained linguistic regularities, and proven to be successful in various natural language processing tasks in recent years. However, existing models for learning word representations focus on either syntagmatic or paradigmatic relations alone. In this paper, we argue that it is beneficial to jointly modeling both relations...