Difference between revisions of "WordNet - RTE Users"

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| When using WordNet, we assume that a term is semantically interchangeable with its
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| When using WordNet, we assume that a term is semantically interchangeable with its exact occurrence, its synonyms, and its hypernyms. In extracting hypernyms, we exclude the hypernyms that are more distant than two links to the original terms in WordNet synsets.
exact occurrence, its synonyms, and its hypernyms. In extracting hypernyms, we exclude the hypernyms that are more distant than two links to the original terms in WordNet synsets.
 
 
| Two ablation tests performed. The first for Wordnet alone, the second for both WordNet and Framenet. Null impact of the resource(s) on two-way task for both ablations.
 
| Two ablation tests performed. The first for Wordnet alone, the second for both WordNet and Framenet. Null impact of the resource(s) on two-way task for both ablations.
  

Revision as of 04:37, 14 December 2009

When not otherwise specified, the data about version, usage and evaluation of the resource have been provided by participants themselves.

Participants* Campaign Version Specific usage description Evaluations / Comments
AUEB RTE5 During the calculation of the similarity measures we treat words from T and H that are synonyms according to WordNet as identical. Ablation test performed. Negative impact of the resource: -2% accuracy on two-way, -2.67% on three-way task.
BIU RTE5 3.0 Synonyms, hyponyms (2 levels away from the original term), the hyponym_instance relation and derivations. Ablation test performed. Positive impact of the resource: +2.5% accuracy on two-way task.
Boeing RTE5 The system makes uses of Wordnet synonyms, hypernyms relationships between (senses of) words, "similar" (SIM), "pertains" (PER), and "derivational" (DER) links to recognize equivalence between T and H. Ablation test performed. Positive impact of the resource: +4% accuracy on two-way, +5.67% on three-way task.
DFKI RTE5 FIRST USE: Argument alignment between T and H.
SECOND USE: used to change all the nominal predicates into verbs, to calculate relatedness between T and H (using VerbOcean).
FIRST USE: Ablation test performed. Impact of the resource: -0.17% accuracy/null respectively on two-way and three-way task for run1; +0.16%/+0.34% for run2; +0.17%/+0.17% for run3.

SECOND USE (WordNet+VerbOcean): null/+0.17% accuracy respectively on two-way and three-way task for run1; +0.5%/+0.67% for run2; +0.17%/+0.17% for run3.

DirRelCond RTE5 Use of many WordNet relations (such as synonymy, hypernymy, hyponymy, meronymy, holonymy etc.) to compute the relatedness between words with the same part of speech in T and H. No ablation test performed. The resource cannot be removed without breaking the functionality of the system.
DLSIUAES RTE5 FIRST USE: Similarity between lemmata, computed by WordNet-based metrics.

SECOND USE: antonymy relations between verbs.
THIRD USE: synonymy/identity between verb lemmata in T and H.
FOURTH USE (WordNet+Framenet): WordNet synonym and hyponym relations from T's frame elements to H's frame elements.

FIRST USE: Ablation test performed. Positive impact of the resource on two-way run: +0.83% accuracy. Negative impact on three-way run: -0.33% accuracy (-0.5% for two-way derived).

SECOND USE (WordNet+VerbOcean+DLSIUAES_negation_list): positive impact on two-way run: +0.66% accuracy. Negative impact on three-way run: -1% (-0.5% for two-way derived).
THIRD USE: No ablation test performed.
FOURTH USE (WordNet+Framenet): positive impact on two-way run: +1.16% accuracy. Negative impact on three-way run: -0.17% (the same for two-way derived).

FBKirst RTE5 3.0 Extraction of a set of 2698 English entailment rules for terms connected by the hyponymy and synonymy relations No ablation test performed
JU_CSE_TAC RTE5 WordNet based Unigram match: if any synset for the H unigram matches with any synset of a word in T then the hypothesis unigram is considered as a WordNet based unigram match. Ablation test performed. Positive impact of the resource: +0.34% on two-way task.
PeMoZa RTE5 FIRST USE: Derivational Morphology.

SECOND USE: Verb Entailment.

Ablation tests performed.

FIRST USE. Impact of the resource on two-way task: -0.5%/+1% accuracy respectively on run1 and run2.
SECOND USE. Impact of the resource on two-way task: +1.33%/-0.33% accuracy respectively on run1 and run2.

QUANTA RTE5 Several relations from wordnet, such as synonyms, hyponym, hypernym et al. Ablation test performed. Negative impact of the resource: -0.17% on two-way task.
Sagan RTE5 Used to obtain two features (string similarity based on Levenshtein distance and semantic similarity) in the training and testing steps of the system. Ablation test performed. Null/negative (-0.87%) impact of the resource respectively on two-way and three-way task.
Siel_09 RTE5 Similarity between nouns using WN tool Ablation test performed. Impact of the resource: +0.34% accuracy on two-way, -0.17% on three-way task.
UAIC RTE5 FIRST USE: Antonymy relation to detect contradiction. In order to broaden the domain of the antonymy relation, we consider a combination of synonyms and antonyms. Used in combination with VerbOcean.

SECOND USE: Synonymy, hyponymy and hypernymy for nouns and adjectives. Used in combination with eXtended WordNet relations.

FIRST USE: Ablation test performed (Wordnet + VerbOcean). Positive impact of the two resources together: +2% accuracy on two-way, +1.5% on three-way task.

SECOND USE: Ablation test performed (Wordnet + eXtended WordNet). Positive impact of the two resources together: +1% accuracy on two-way, +1.33% on three-way task.

UB.dmirg RTE5 When using WordNet, we assume that a term is semantically interchangeable with its exact occurrence, its synonyms, and its hypernyms. In extracting hypernyms, we exclude the hypernyms that are more distant than two links to the original terms in WordNet synsets. Two ablation tests performed. The first for Wordnet alone, the second for both WordNet and Framenet. Null impact of the resource(s) on two-way task for both ablations.
AUEB RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
BIU RTE4 3.0 Synonyms, hyponyms (2 levels away from the original term), the hyponym_instance relation and derivations. Also used as part of our novel lexical-syntactic resource 0.8% improvement in ablation test on RTE-4. Potential contribution is higher since this resource partially overlaps with the novel lexical-syntactic rule base
Boeing RTE4 2.0 Semantic relation between words No formal evaluation. Plays a role in most entailments found
Cambridge RTE4 3.0 Meaning postulates from WordNet noun hyponymy, e.g. forall x: cat(x) -> animal(x) No systematic evaluation
CERES RTE4 3.0 Hypernyms, antonyms, indexWords (N,V,Adj,Adv) Used, but no evaluation performed
DFKI RTE4 3.0 Semantic relation between words No separate evaluation
DLSIUAES RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
EMORY RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
FbkIrst RTE4 3.0 Lexical similarity No precise evaluation of the resource has been carried out. In our second run we used a combined system (EDITSneg + EDITSallbutneg), and we had an improvement of 0.6% in accuracy with respect to the first run in which only EDITSneg was used. EDITSallbutneg exploits lexical similarity (WordNet similarity), but we can’t affirm with precision that the improvement is due only to the use of WordNet
FSC RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
IIT RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
IPD RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
OAQA RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
QUANTA RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
SAGAN RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
Stanford RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
UAIC RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
UMD RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
UNED RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
Uoeltg RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
UPC RTE4 Data taken from the RTE4 proceedings. Participants are recommended to add further information.
AUEB RTE3 2.1 Synonymy resolution Replacing the words of H with their synonyms in T: on RTE3 data sets 2% improvement
UIUC RTE3 Semantic distance between words
VENSES RTE3 3.0 Semantic relation between words No evaluation of the resource
New user Participants are encouraged to contribute.
Total: 24


[*] For further information about participants, click here: RTE Challenges - Data about participants

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