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Predicting correctness of eyewitness statements using the Semantic Evaluation Method (SEM)

Sarwar, Farhan LU ; Sikström, Sverker LU ; Allwood, Carl Martin LU and Innes-Ker, Åse LU (2015) In Quality & Quantity 49(4). p.1735-1745
Abstract
Evaluating the correctness of eyewitness statements is one of the biggest challenges for the legal system, and this task is currently typically performed by human evaluations. Here we study whether a computational method could be applied to discriminate between correct and incorrect statements. The Semantic Evaluation Method (SEM) is based on Latent Semantic Analysis (LSA, Landauer & Dumais, 1997), - a method for automatically generating high dimensional semantic representations of words and sentences. The verbal data was extracted from the recorded narratives from a prior eyewitness study investigating the role of repeated retellings on subsequent recall accuracy and confidence (Sarwar, Allwood, & Innes-Ker, 2011). Participants... (More)
Evaluating the correctness of eyewitness statements is one of the biggest challenges for the legal system, and this task is currently typically performed by human evaluations. Here we study whether a computational method could be applied to discriminate between correct and incorrect statements. The Semantic Evaluation Method (SEM) is based on Latent Semantic Analysis (LSA, Landauer & Dumais, 1997), - a method for automatically generating high dimensional semantic representations of words and sentences. The verbal data was extracted from the recorded narratives from a prior eyewitness study investigating the role of repeated retellings on subsequent recall accuracy and confidence (Sarwar, Allwood, & Innes-Ker, 2011). Participants watched a film of a kidnapping and then either retold the events to a single listener, or discussed the content with a confederate at five separate times over a 20-day period. Their subsequent written recall was analyzed using the Semantic Evaluation Method (SEM). The results show that accuracy can be predicted from quantification of the semantic content of eyewitness memory reports using SEM. This result also held true when data was separated into three distinct categories and the SEM was trained and tested on different categories of data. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Eyewitnesses’ correctness, semantic evaluation method, semantic spaces, consistency.
in
Quality & Quantity
volume
49
issue
4
pages
1735 - 1745
publisher
Springer
external identifiers
  • wos:000355922200027
  • scopus:84930756906
ISSN
1573-7845
DOI
10.1007/s11135-014-9997-7
language
English
LU publication?
yes
id
04637c97-c15d-47b2-95cf-e31489625c4b (old id 4250764)
date added to LUP
2014-01-20 13:24:22
date last changed
2017-05-14 03:16:11
@article{04637c97-c15d-47b2-95cf-e31489625c4b,
  abstract     = {Evaluating the correctness of eyewitness statements is one of the biggest challenges for the legal system, and this task is currently typically performed by human evaluations. Here we study whether a computational method could be applied to discriminate between correct and incorrect statements. The Semantic Evaluation Method (SEM) is based on Latent Semantic Analysis (LSA, Landauer & Dumais, 1997), - a method for automatically generating high dimensional semantic representations of words and sentences. The verbal data was extracted from the recorded narratives from a prior eyewitness study investigating the role of repeated retellings on subsequent recall accuracy and confidence (Sarwar, Allwood, & Innes-Ker, 2011). Participants watched a film of a kidnapping and then either retold the events to a single listener, or discussed the content with a confederate at five separate times over a 20-day period. Their subsequent written recall was analyzed using the Semantic Evaluation Method (SEM). The results show that accuracy can be predicted from quantification of the semantic content of eyewitness memory reports using SEM. This result also held true when data was separated into three distinct categories and the SEM was trained and tested on different categories of data.},
  author       = {Sarwar, Farhan and Sikström, Sverker and Allwood, Carl Martin and Innes-Ker, Åse},
  issn         = {1573-7845},
  keyword      = {Eyewitnesses’ correctness,semantic evaluation method,semantic spaces,consistency.},
  language     = {eng},
  number       = {4},
  pages        = {1735--1745},
  publisher    = {Springer},
  series       = {Quality & Quantity},
  title        = {Predicting correctness of eyewitness statements using the Semantic Evaluation Method (SEM)},
  url          = {http://dx.doi.org/10.1007/s11135-014-9997-7},
  volume       = {49},
  year         = {2015},
}