| other,6-1-P06-2110,ak | This paper examines what kind of <term> similarity </term> between <term> words </term> can be represented by what kind of <term> word vectors </term> in the <term> vector space model </term> . | #12413 This paper examines what kind ofsimilarity between words can be represented by what kind of word vectors in the vector space model. | |
| other,8-1-P06-2110,ak | This paper examines what kind of <term> similarity </term> between <term> words </term> can be represented by what kind of <term> word vectors </term> in the <term> vector space model </term> . | #12415 This paper examines what kind of similarity betweenwords can be represented by what kind of word vectors in the vector space model. | |
| model,16-1-P06-2110,ak | This paper examines what kind of <term> similarity </term> between <term> words </term> can be represented by what kind of <term> word vectors </term> in the <term> vector space model </term> . | #12423 This paper examines what kind of similarity between words can be represented by what kind ofword vectors in the vector space model. | |
| tech,20-1-P06-2110,ak | This paper examines what kind of <term> similarity </term> between <term> words </term> can be represented by what kind of <term> word vectors </term> in the <term> vector space model </term> . | #12427 This paper examines what kind of similarity between words can be represented by what kind of word vectors in thevector space model. | |
| model,8-2-P06-2110,ak | Through two experiments , three methods for constructing <term> word vectors </term> , i.e. , <term> LSA-based , cooccurrence-based and dictionary-based methods </term> , were compared in terms of the ability to represent two kinds of <term> similarity </term> , i.e. , <term> taxonomic similarity </term> and <term> associative similarity </term> . | #12439 Through two experiments, three methods for constructingword vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. | |
| tech,13-2-P06-2110,ak | Through two experiments , three methods for constructing <term> word vectors </term> , i.e. , <term> LSA-based , cooccurrence-based and dictionary-based methods </term> , were compared in terms of the ability to represent two kinds of <term> similarity </term> , i.e. , <term> taxonomic similarity </term> and <term> associative similarity </term> . | #12444 Through two experiments, three methods for constructing word vectors, i.e.,LSA-based , cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. | |
| other,32-2-P06-2110,ak | Through two experiments , three methods for constructing <term> word vectors </term> , i.e. , <term> LSA-based , cooccurrence-based and dictionary-based methods </term> , were compared in terms of the ability to represent two kinds of <term> similarity </term> , i.e. , <term> taxonomic similarity </term> and <term> associative similarity </term> . | #12463 Through two experiments, three methods for constructing word vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds ofsimilarity, i.e., taxonomic similarity and associative similarity. | |
| other,36-2-P06-2110,ak | Through two experiments , three methods for constructing <term> word vectors </term> , i.e. , <term> LSA-based , cooccurrence-based and dictionary-based methods </term> , were compared in terms of the ability to represent two kinds of <term> similarity </term> , i.e. , <term> taxonomic similarity </term> and <term> associative similarity </term> . | #12467 Through two experiments, three methods for constructing word vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e.,taxonomic similarity and associative similarity. | |
| other,39-2-P06-2110,ak | Through two experiments , three methods for constructing <term> word vectors </term> , i.e. , <term> LSA-based , cooccurrence-based and dictionary-based methods </term> , were compared in terms of the ability to represent two kinds of <term> similarity </term> , i.e. , <term> taxonomic similarity </term> and <term> associative similarity </term> . | #12470 Through two experiments, three methods for constructing word vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity andassociative similarity. | |
| model,8-3-P06-2110,ak | The result of the comparison was that the <term> dictionary-based word vectors </term> better reflect <term> taxonomic similarity </term> , while the <term> LSA-based and the cooccurrence-based word vectors </term> better reflect <term> associative similarity </term> . | #12481 The result of the comparison was that thedictionary-based word vectors better reflect taxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity. | |
| other,13-3-P06-2110,ak | The result of the comparison was that the <term> dictionary-based word vectors </term> better reflect <term> taxonomic similarity </term> , while the <term> LSA-based and the cooccurrence-based word vectors </term> better reflect <term> associative similarity </term> . | #12486 The result of the comparison was that the dictionary-based word vectors better reflecttaxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity. | |
| model,18-3-P06-2110,ak | The result of the comparison was that the <term> dictionary-based word vectors </term> better reflect <term> taxonomic similarity </term> , while the <term> LSA-based and the cooccurrence-based word vectors </term> better reflect <term> associative similarity </term> . | #12491 The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while theLSA-based and the cooccurrence-based word vectors better reflect associative similarity. | |
| other,26-3-P06-2110,ak | The result of the comparison was that the <term> dictionary-based word vectors </term> better reflect <term> taxonomic similarity </term> , while the <term> LSA-based and the cooccurrence-based word vectors </term> better reflect <term> associative similarity </term> . | #12499 The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflectassociative similarity. |
