[PDF] Top 20 Recognizing Textual Entailment in Twitter Using Word Embeddings

... Since the contradiction manifested between tweet pairs labeled was very fine grained, we ex- pected bag of word models to perform poorly and confusion between entailment and contradiction to be high. Also, ... See full document

... mographic word embeddings following the initial findings of Bamman et ...These embeddings are computed on a cor- pus specifically built for racism detection and ob- tained by massive gathering of ... See full document

... that word in the two corpora. In Twitter, “bc” is frequently an abbreviation for “because”, while in Wikipedia “bc” is more com- monly used as part of dates, ...in Twitter “ill” is often a ... See full document

... ing entailment relations have employed classi- fiers over hand engineered lexicalized features, or deep learning models that implicitly cap- ture lexicalization through word ...determine entailment ... See full document

... Set of hybrid approaches used a combination of methods to recognize textual Entailment. Hybrid approaches are usually based on only two methods with one acting as primary strategy and the other as a backup. ... See full document

... In the creation of new cost schemes, users can express edit operation costs, and conditions over the A and B elements, using a meta-language based on a lisp-like syntax (e.g. (+ (IDF A) (IDF B)), (not (equals A ... See full document

... select the RTE pairs in which we find a tree skele- ton and match an inference rule. The first number in our table entries represents how many of such pairs we have identified, out the 1600 of devel- opment and test ... See full document

... our Word- Net lexical chains module which lead to fewer un- sound axioms and incorporated in our logic prover semantic and temporal axioms which decrease its dependence on world ...high word overlap and ... See full document

... net, because it could provide additional semantic information improving our semantic features, and so the performance of our system. Due to being an expensive and not freely available resource, we are avoiding ... See full document

... Table 4 shows the performance which is achieved by the same NN models when all words of premise sentences are replaced by unknown word symbols. Because this replace- ment removes all context information from ... See full document

... The second resource which we have used as a source of axioms is FrameNet, release 1.5, see Rup- penhofer et al. (2006). FrameNet has a shorter history in NLP applications than WordNet, but lately more and more ... See full document

... of entailment rules and coref- erence substitutions are yet, in most cases, insuffi- cient in transforming T into H, our system allows on-the-fly ...serted word, and its probability estimation according to ... See full document

... dataset using lexical match and mismatch ...features using logical inference to build a hybrid model that achieves an accuracy of ...that using task label as feature in their model increases the ... See full document

... We created a metric based on Wordnet to try to capture the semantic similarity between T and H to sentence level. Longest common substring is selected because is easy to implement and provides a good measure for ... See full document

... our entailment classi- fier with 500 trees and feature value standardiza- tion, trained and evaluated on those T-H pairs for which ccg2lambda outputs unknown (around 71% of the ... See full document

... free word or- der as well as its diglossic nature (where the stan- dard and the dialects mix in most genres of ...large Word- Net (Miller, 1995) or a resource such as VerbO- cean (Chklovski and Pantel, ... See full document

... the word overlap between the two text strings. Using either statistical or Word- Net’s relations, almost all systems considered lexical relationships that indicate ...for entailment by several ... See full document

... In order to tackle partial entailment, we need to find a way to decompose a hypothesis. Nielsen et al. (2009) defined a model of facets, where each such facet is a pair of words in the hypothesis and the direct ... See full document

... The conventional alignment-based RTE method measures the quality of the alignment between the premise P and the hypothesis H to predict their entailment relation (Fig. 3a). An automated aligner is first learned ... See full document

... make entailment judgments over isolated T–H pairs drawn from multiple ...Evaluation using Textual Entailment challenge [10] was organized at ... See full document