Index of papers in Proc. ACL 2013 that mention
  • similarity score
Melamud, Oren and Berant, Jonathan and Dagan, Ido and Goldberger, Jacob and Szpektor, Idan
Experimental Settings
Table 2: Context-sensitive similarity scores (in bold) for the Y slots of four rule applications.
Introduction
Rather than computing a single context-insensitive rule score, we compute a distinct word-level similarity score for each topic in an LDA model.
Two-level Context-sensitive Inference
At learning time, we compute for each candidate rule a separate, topic-biased, similarity score per each of the topics in the LDA model.
Two-level Context-sensitive Inference
Then, at rule application time, we compute an overall reliability score for the rule by combining the per-topic similarity scores , while biasing the score combination according to the given context of 212.
Two-level Context-sensitive Inference
sim/3m), we compute a topic-biased similarity score for each LDA topic 75, denoted by simt(v, v’ simt(v, v’) is computed by applying
similarity score is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Bond, Francis and Foster, Ryan
Extending with non-wordnet data
Meyer and Gurevych (2011) showed that automatic alignments between Wiktionary senses and PWN can be established with reasonable accuracy and recall by combining multiple text similarity scores to compare a bag of words based on several pieces of information linked to a WordNet sense with another bag of words obtained from a Wiktionary entry.
Extending with non-wordnet data
We calculated a number of similarity scores , the first two based on similarity in the number of lemmas, calculated using the J accard index:
Extending with non-wordnet data
This development dataset was used to tune refined similarity scores .
similarity score is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Hewavitharana, Sanjika and Mehay, Dennis and Ananthakrishnan, Sankaranarayanan and Natarajan, Prem
Incremental Topic-Based Adaptation
We define the similarity score as sim(6di, 661*) = 1 — JSD(6di||6d*).1 Thus, we obtain a vector of similarity scores indexed by the training conversations.
Incremental Topic-Based Adaptation
X —> Y added to the search graph, its topic similarity score as follows:
Incremental Topic-Based Adaptation
Phrase pairs from the “background conversation” only are assigned a similarity score FX_>y = 0.00.
similarity score is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Veale, Tony and Li, Guofu
Empirical Evaluation
Rex estimates a similarity score for each of the 1,264,827 pairings of comparable terms it finds in the Google 3-grams.
Related Work and Ideas
Negating the log of this normalized length yields a corresponding similarity score .
Summary and Conclusions
Using the Google n-grams as a source of tacit grouping constructions, we have created a comprehensive lookup table that provides Rex similarity scores for the most common (if often implicit) comparisons.
Summary and Conclusions
Comparability is not the same as similarity, and a nonzero similarity score does not mean that two concepts would ever be considered comparable by a human.
similarity score is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Chen, Boxing and Kuhn, Roland and Foster, George
Abstract
Then, for each phrase pair extracted from the training data, we create a vector with features defined in the same way, and calculate its similarity score with the vector representing the dev set.
Vector space model adaptation
VSM uses the similarity score between the vec-
Vector space model adaptation
To further improve the similarity score , we apply absolute discounting smoothing when calculating the probability distributions p,( f, e).
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Dasgupta, Anirban and Kumar, Ravi and Ravi, Sujith
Using the Framework
For each pair of nodes (u,v) in the graph, we compute the semantic similarity score (using WordNet) between every pair of dependency relation (rel: a, b) in u and v as: s(u,v) = Z WN(a,-,aj) >< WN(b,-,bj),
Using the Framework
WN(w,—, wj) is defined as the WordNet similarity score between words 212,- and to]?
Using the Framework
For example, the sentences “I adore tennis” and “Everyone likes tennis” convey the same view and should be assigned a higher similarity score as opposed to “I hate tennis”.
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
He, Zhengyan and Liu, Shujie and Li, Mu and Zhou, Ming and Zhang, Longkai and Wang, Houfeng
Learning Representation for Contextual Document
In the pre-training stage, Stacked Denoising Auto-encoders are built in an unsupervised layer-wise fashion to discover general concepts encoding d and e. In the supervised fine-tuning stage, the entire network weights are fine-tuned to optimize the similarity score sim(d, e).
Learning Representation for Contextual Document
The similarity score of (d, 6) pair is defined as the dot product of f (d) and f (6) (Fig.
Learning Representation for Contextual Document
That is, we raise the similarity score of true pair sim(d, e) and penalize all the rest sim(d, 6,).
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Quan, Xiaojun and Kit, Chunyu and Song, Yan
Methodology 2.1 The Problem
All of these high-affinity pairs have a similarity score higher than 0.72.
Methodology 2.1 The Problem
These two sets of similarity scores are then plotted in a scatter plot, as in Figure 4.
Methodology 2.1 The Problem
Then, the relation matrix of a bitext is built of similarity scores for the rough translation and the actual translation at sentence level.
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Ravi, Sujith
Bayesian MT Decipherment via Hash Sampling
One possible strategy is to compute similarity scores 8(Wfi, we/) between the current source word feature vector Wfi and feature vectors we/Eve for all possible candidates in the target vocabulary.
Bayesian MT Decipherment via Hash Sampling
Following this, we can prune the translation candidate set by keeping only the top candidates 6* according to the similarity scores .
Bayesian MT Decipherment via Hash Sampling
This makes the complexity far worse (in practice) since the dimensionality of the feature vectors d is a much higher value than Computing similarity scores alone (nai'vely) would incur O(|Ve| - d) time which is prohibitively huge since we have to do this for every token in the source language corpus.
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
You, Gae-won and Cha, Young-rok and Kim, Jinhan and Hwang, Seung-won
Preliminaries
To integrate two similarity scores , we adopt an average as a composite function.
Preliminaries
We finally compute initial similarity scores for all pairs (6, c) where e 6 V6 and c 6 VC, and build the initial similarity matrix R0.
Preliminaries
From R”, we finally extract one-to-one matches by using simple greedy approach of three steps: (1) choosing the pair with the highest similarity score ; (2) removing the corresponding row and column from R”; (3) repeating (l) and (2) until the matching score is not less than a threshold 6.
similarity score is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: