Arrow Research search

Author name cluster

Stefan Trausan-Matu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
2 author rows

Possible papers

4

AAAI Conference 2016 Conference Paper

Age of Exposure: A Model of Word Learning

  • Mihai Dascalu
  • Danielle McNamara
  • Scott Crossley
  • Stefan Trausan-Matu

Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a largescale learning corpus (i. e. , TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.

PDF Details

IJCAI Conference 2011 Conference Paper

Improving Topic Evaluation Using Conceptual Knowledge

  • Claudiu Cristian Musat
  • Julien Velcin
  • Stefan Trausan-Matu
  • Marian-Andrei Rizoiu

The growing number of statistical topic models led to the need to better evaluate their output. Traditional evaluation means estimate the model's fitness to unseen data. It has recently been proven than the output of human judgment can greatly differ from these measures. Thus the need for methods that better emulate human judgment is stringent. In this paper we present a system that computes the usefulness of individual topics from a given model on the basis of information drawn from a given ontology, in this case WordNet. The notion of utility is regarded as the ability to attribute a concept to each topic and separate words related to the topic from the unrelated ones based on that concept. In multiple experiments we prove the correlation between the automatic evaluation method and the answers received from human evaluators, for various corpora and difficulty levels. By changing the evaluation focus from a statistical one to a conceptual one we were able to detect which topics are conceptually meaningful and rank them accordingly.

PDF Details DOI
v2026.09.13