Statistical and Semantic Similarity between English Sentences
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1
Statistical and Semantic Similarity between English Sentences (2014)
~EN USISBN: 9783659616389 bzw. 3659616389, vermutlich in Englisch, 72 Seiten, gebraucht.
This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research's sentence-level paraphrase dataset [Rus et al., 2012]. On O'Shea's data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms, 152x220, 0.129kg, 22.900/15.200/0.400.
2
Statistical and Semantic Similarity between English Sentences
DE NWISBN: 9783659616389 bzw. 3659616389, in Deutsch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten.
This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research's sentence-level paraphrase dataset [Rus et al., 2012]. On O'Shea's data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms.
This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research's sentence-level paraphrase dataset [Rus et al., 2012]. On O'Shea's data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms.
3
Statistical and Semantic Similarity between English Sentences (2012)
~EN NW ABISBN: 9783659616389 bzw. 3659616389, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Schweiz, Lieferzeit: 2 Tage, zzgl. Versandkosten.
This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research's sentence-level paraphrase dataset [Rus et al., 2012]. On O'Shea's data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms.
This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O'Shea's set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research's sentence-level paraphrase dataset [Rus et al., 2012]. On O'Shea's data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms.
4
Statistical and Semantic Similarity between English Sentences (2012)
DE PB NWISBN: 9783659616389 bzw. 3659616389, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Statistical and Semantic Similarity between English Sentences: This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O`Shea`s set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research`s sentence-level paraphrase dataset [Rus et al., 2012]. On O`Shea`s data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms, Englisch, Taschenbuch.
Statistical and Semantic Similarity between English Sentences: This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O`Shea`s set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research`s sentence-level paraphrase dataset [Rus et al., 2012]. On O`Shea`s data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms, Englisch, Taschenbuch.
5
Statistical and Semantic Similarity between English Sentences
DE PB NWISBN: 3659616389 bzw. 9783659616389, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
6
Statistical and Semantic Similarity between English Sentences (2014)
~EN PB NWISBN: 9783659616389 bzw. 3659616389, vermutlich in Englisch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Statistical and Semantic Similari (2014)
DE PB NWISBN: 9783659616389 bzw. 3659616389, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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