| Abstract | We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. |
| Conclusions | We adopt three cohesion metrics, clue words, semantic similarity and cosine similarity , to measure the weight of the edges. |
| Empirical Evaluation | In Section 3.3, we developed three ways to compute the weight of an edge in the sentence quotation graph, i.e., clue words, semantic similarity based on WordNet and cosine similarity . |
| Empirical Evaluation | The widely used cosine similarity does not perform well. |
| Empirical Evaluation | The above experiments show that the widely used cosine similarity and the more sophisticated semantic similarity in WordNet are less accurate than the basic CWS in the summarization framework. |
| Extracting Conversations from Multiple Emails | and (3) cosine similarity that is based on the word TFIDF vector. |
| Extracting Conversations from Multiple Emails | 3.3.3 Cosine Similarity |
| Extracting Conversations from Multiple Emails | Cosine similarity is a popular metric to compute the similarity of two text units. |
| Introduction | (Carenini et al., 2007), semantic similarity and cosine similarity . |
| Summarization Based on the Sentence Quotation Graph | In the rest of this paper, let CWS denote the Generalized ClueWordSummarizer when the edge weight is based on clue words, and let CWS-Cosine and CWS-Semantic denote the summarizer when the edge weight is cosine similarity and semantic similarity respectively. |
| Context and Answer Detection | The word similarity is based on cosine similarity of TF/IDF weighted vectors. |
| Context and Answer Detection | - Cosine similarity with the question |
| Context and Answer Detection | - Cosine similarity between contiguous sentences |
| Related Work | (2006a) used cosine similarity to match students’ query with reply posts for discussion-bot. |
| Introduction | For clustering we use a number of word similarities like cosine similarity among words and co-occurrence, along with the k-means clustering algorithm. |
| Word Clustering | 3.1 Cosine Similarity based on Sentence Level Co-occurrence |
| Word Clustering | Then we measure cosine similarity between the word vectors. |
| Word Clustering | The cosine similarity between two word vectors (fl and E”) with dimension d is measured as: |
| Acquiring Paraphrases | We use cosine similarity , which |
| Acquiring Paraphrases | As described in Section 3.2, we find paraphrases of a phrase p,- by finding its nearest neighbors based on cosine similarity between the feature vector of pi and other phrases. |
| Acquiring Paraphrases | If n is the number of vectors and d is the dimensionality of the vector space, finding cosine similarity between each pair of vectors has time complexity 0(n2 d). |
| Finding the Homographs in a Lexicon | Cohesiveness Score: Mean of the cosine similarities between each pair of definitions of w. |
| Finding the Homographs in a Lexicon | Average Number of Null Similarities: The number of definition pairs that have zero cosine similarity score (no word overlap). |
| Finding the Homographs in a Lexicon | The last feature sorts the pairwise cosine similarity scores in ascending order, prunes the top n% of the scores, and uses the maximum remaining score as the feature value. |
| Features | Cosine similarity between the document vector representations is probably the easiest and most commonly used among the various similarity measures. |
| Features | The cosine similarity between two (document representation) vectors v1 and 212 is given by 0036 = W. A value of 0 indicates that the vectors are orthogonal and dissimilar, a value of 1 indicates perfectly similar documents in terms of the words con- |
| Features | To compute the cosine overlap features, we find the pairwise cosine similarity between each two documents in an input set and compute their average. |