[PDF] Top 20 Probabilistic Soft Logic for Semantic Textual Similarity
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Probabilistic Soft Logic for Semantic Textual Similarity
... Markov Logic Networks (MLN) (Richardson and Domingos, 2006) are a framework for probabilis- tic logic that employ weighted formulas in first- order logic to compactly encode complex undi- rected ... See full document
10
Determining Semantic Textual Similarity using Natural Deduction Proofs
... Third, we attempt to prove entailment relations between sentence pairs. For this purpose, we use Coq (Bertot and Castran, 2010), which can be used for efficient theorem-proving for natural lan- guage inference using both ... See full document
11
A Semantically Enhanced Approach to Determine Textual Similarity
... a logic form transformation derived from dependency parses and named ...three logic from transformations, use a modified resolution step and extract hundreds of features from the ...modified logic ... See full document
11
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA
... one semantic similarity task at the same ...a soft alignment which computes atten- tion weights to determine the relevance between two input ... See full document
10
Semantic Parsing using Distributional Semantics and Probabilistic Logic
... Markov Logic Network (MLN) (Richardson and Domingos, 2006) is a framework for probabilis- tic logic that employ weighted formulas in first- order logic to compactly encode complex undi- rected ... See full document
5
Correlation Coefficients and Semantic Textual Similarity
... a probabilistic treatment ...about semantic similarity in terms of correlations between random variables and make the connection to the widely used co- sine ... See full document
12
A Logic Based Semantic Approach to Recognizing Textual Entailment
... with more than one HYPONYMY relations. Al- though these relations link semantically related concepts, the type of semantic similarity they in- troduce is not suited for inferences. Another re- striction ... See full document
8
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics
... like Textual Entailment (Dagan et al., 2013), Semantic Parsing (Kwiatkowski et ...by logic-based ...of logic-based ...uncertain, probabilistic in- formation (Garrette et ... See full document
11
Learning the Impact and Behavior of Syntactic Structure: A Case Study in Semantic Textual Similarity
... Complex logical representations are usually used for semantic inference tasks. Nevertheless, due to the high cost of constructing complex logical representations, practical applications usually sup- port shallower ... See full document
9
If Sentences Could See: Investigating Visual Information for Semantic Textual Similarity
... We next run a deep convolutional neural network (CNN) pre-trained on the ImageNet classification task (Russakovsky et al., 2015) and extract the 4096- dimensional vector from the pre-softmax layer to represent each ... See full document
15
Rule based vs Neural Net Approaches to Semantic Textual Similarity
... a semantic difference vector by concatenating the element-wise absolute difference and the element-wise multiplication of the corresponding sentence ...the semantic differ- ence vector into a fully ... See full document
6
Frequently Asked Questions Retrieval for Croatian Based on Semantic Textual Similarity
... LSA semantic similarity (LSA). Latent seman- tic analysis (LSA), first introduced by Deerwester et al. (1990), has been shown to be very effective for computing word and document similarity. To build ... See full document
10
Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity
... Another observation concerns the level of argu- ment granularity. In the previous analysis, we used the gold number of clusters. We note, however, that the level of granularity is to a certain extent arbitrary. To ... See full document
6
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity
... Next, we combine the outputs of these four met- rics to build eight different regression models us- ing different classification algorithms in WEKA (e.g. IsotonicRegression, LeastMedSq, Mul- tilayerPerceptron, ... See full document
6
Cross lingual Learning of Semantic Textual Similarity with Multilingual Word Representations
... assessing semantic content of two sentences notably does not take important se- mantic features such as negation into account, and can therefore be seen as complimentary to textual ...the semantic ... See full document
5
Extending Monolingual Semantic Textual Similarity Task to Multiple Cross lingual Settings
... monolingual semantic textual similarity (STS) task setting to multiple cross-lingual settings involving English, Japanese, and ...“monolingual similarity after translation” strategy to predict ... See full document
7
DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Recognizing Textual Entailment (RTE) is an important task in many natural language ...distributional semantic model (DSM) in RTE ...distributional semantic models (DSM) generated using Word2Vec and ... See full document
8
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
... A related problem to parameter learning is structure learning, i.e., identifying an ac- curate dependency structure for a model. A common SRL approach is searching over the space of templates for PGMs. For ... See full document
67
Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
... Related semantic units in two sentences must be similar or related in their meaning, and 2) Commonalities in their se- mantic contexts in the respective sentences provide additional evidence of their relatedness ... See full document
12
HYBRID SOFT COMPUTING: THE PERCEPTION
... There is a wide scope of industrial and commercial problems that require the analysis of uncertain and imprecise information. Usually, an incomplete understanding of the problem domain further compounds the problem of ... See full document
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