scholarly journals Evaluating WordNet-based Measures of Lexical Semantic Relatedness

2006 ◽  
Vol 32 (1) ◽  
pp. 13-47 ◽  
Author(s):  
Alexander Budanitsky ◽  
Graeme Hirst
Keyword(s):  
Information Content ◽  
Semantic Relatedness ◽  
Real Word ◽  
Spelling Errors ◽  
Lexical Semantic ◽  
Distributional Similarity ◽  
Central Resource ◽  
Word Spelling

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.

Correcting real-word spelling errors by restoring lexical cohesion

2005 ◽  
Vol 11 (1) ◽  
pp. 87-111 ◽  
Author(s):  
GRAEME HIRST ◽  
ALEXANDER BUDANITSKY
Keyword(s):  
Semantic Distance ◽  
Real Word ◽  
Spelling Errors ◽  
Lexical Cohesion ◽  
Word Spelling ◽  
Spelling Checker

Spelling errors that happen to result in a real word in the lexicon cannot be detected by a conventional spelling checker. We present a method for detecting and correcting many such errors by identifying tokens that are semantically unrelated to their context and are spelling variations of words that would be related to the context. Relatedness to context is determined by a measure of semantic distance initially proposed by Jiang and Conrath (1997). We tested the method on an artificial corpus of errors; it achieved recall of 23–50% and precision of 18–25%.

Correcting real-word spelling errors: A new hybrid approach

2017 ◽  
Vol 33 (3) ◽  
pp. 488-499 ◽  
Author(s):  
Seyed MohammadSadegh Dashti ◽  
Amid Khatibi Bardsiri ◽  
Vahid Khatibi Bardsiri
Keyword(s):  
Hybrid Approach ◽  
Real Word ◽  
Spelling Errors ◽  
Word Spelling

To Predict or Not to Predict: Influences of Task and Strategy on the Processing of Semantic Relations

2007 ◽  
Vol 19 (8) ◽  
pp. 1259-1274 ◽  
Author(s):  
Dietmar Roehm ◽  
Ina Bornkessel-Schlesewsky ◽  
Frank Rösler ◽  
Matthias Schlesewsky
Keyword(s):  
Semantic Relatedness ◽  
Presentation Mode ◽  
Judgment Task ◽  
Sentence Context ◽  
Task Demands ◽  
Event Related Potential ◽  
Semantic Relations ◽  
Decision Task ◽  
Lexical Semantic ◽  
Experimental Environment

We report a series of event-related potential experiments designed to dissociate the functionally distinct processes involved in the comprehension of highly restricted lexical-semantic relations (antonyms). We sought to differentiate between influences of semantic relatedness (which are independent of the experimental setting) and processes related to predictability (which differ as a function of the experimental environment). To this end, we conducted three ERP studies contrasting the processing of antonym relations (black-white) with that of related (black-yellow) and unrelated (black-nice) word pairs. Whereas the lexical-semantic manipulation was kept constant across experiments, the experimental environment and the task demands varied: Experiment 1 presented the word pairs in a sentence context of the form The opposite of X is Y and used a sensicality judgment. Experiment 2 used a word pair presentation mode and a lexical decision task. Experiment 3 also examined word pairs, but with an antonymy judgment task. All three experiments revealed a graded N400 response (unrelated > related > antonyms), thus supporting the assumption that semantic associations are processed automatically. In addition, the experiments revealed that, in highly constrained task environments, the N400 gradation occurs simultaneously with a P300 effect for the antonym condition, thus leading to the superficial impression of an extremely “reduced” N400 for antonym pairs. Comparisons across experiments and participant groups revealed that the P300 effect is not only a function of stimulus constraints (i.e., sentence context) and experimental task, but that it is also crucially influenced by individual processing strategies used to achieve successful task performance.

Recent advances in methods of lexical semantic relatedness – a survey

2012 ◽  
Vol 19 (4) ◽  
pp. 411-479 ◽  
Author(s):  
ZIQI ZHANG ◽  
ANNA LISA GENTILE ◽  
FABIO CIRAVEGNA
Keyword(s):  
Language Processing ◽  
Semantic Relatedness ◽  
Background Information ◽  
Future Research ◽  
Review Of The Literature ◽  
Research Directions ◽  
Lexical Semantic ◽  
Research Activities ◽  
Domain Boundaries ◽  
Future Research Directions

AbstractMeasuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

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