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Jaywalking is crossing the street someplace other than at an intersection or crosswalk. And it is probably unlawful. You’re in a rush and https://en.wikiquote.org/wiki/User:CensoredScribe don’t need to head all of the approach to the crosswalk to cross the street. Anyway, who cares, proper? The store you need is instantly across the street — not anyplace near the intersection. So you go ahead. Cross when site visitors is clear. What you’ve simply achieved is jaywalked — crossed the street someplace apart from at an intersection or crosswalk. And it is probably unlawful. Mostly it has to do with pedestrian security typically. Furthermore, whereas pedestrians characterize only 3 % of those involved in traffic incidents, they account for 14 % of site visitors deaths. Even though 70 p.c of pedestrian fatalities are from accidents outside of intersections, many are at intersections and crosswalks the place pedestrian crossings are concentrated. So, jaywalking is against the law for security causes.

On this work, we current an method based on combining string kernels and phrase embeddings for computerized essay scoring. String kernels seize the similarity amongst strings based mostly on counting widespread character n-grams, that are a low-degree but highly effective kind of characteristic, demonstrating state-of-the-artwork results in varied textual content classification tasks comparable to Arabic dialect identification or native language identification. To our best information, we’re the first to apply string kernels to routinely rating essays. We are also the first to combine them with a high-level semantic feature illustration, particularly the bag-of-super-word-embeddings. We report the perfect performance on the Automated Student Assessment Prize data set, in each in-domain and cross-domain settings, surpassing latest state-of-the-art deep studying approaches. Automatic essay scoring (AES) is the task of assigning grades to essays written in an academic setting, utilizing a pc-based mostly system with pure language processing capabilities. The aim of designing such techniques is to scale back the involvement of human graders so far as possible. AES is a difficult job because it depends on grammar as well as semantics, pragmatics and discourse Song et al. 2017). Although traditional AES methods typically depend on handcrafted features Larkey (1998); Foltz et al. 1999); Attali and Burstein (2006); Dikli (2006); Wang and Brown (2008); Chen and He (2013); Somasundaran et al. 2014); Yannakoudakis et al. 2014); Phandi et al. 2015), latest results point out that state-of-the-artwork deep studying strategies attain higher performance Alikaniotis et al. 2016); Dong and Zhang (2016); Taghipour and Ng (2016); Dong et al. 2017); Song et al. 2017); Tay et al. On this paper, we propose to mix string kernels (low-level character n-gram features) and word embeddings (excessive-degree semantic options) to acquire state-of-the-art AES outcomes. Since current methods primarily based on string kernels have demonstrated exceptional efficiency in numerous textual content classification tasks starting from authorship identification Popescu and Grozea (2012) and sentiment evaluation Giménez-Pérez et al. 2017); Popescu et al. 2017) to native language identification Popescu. Ionescu (2013); Ionescu et al. 2014); Ionescu (2015); Ionescu et al. 2016); Ionescu and Popescu (2017) and dialect identification Ionescu and Popescu (2016); Ionescu and Butnaru (2017), we believe that string kernels can reach equally good leads to AES. To the best of our information, string kernels have by no means been used for this task. As string kernels are a easy strategy that depends solely on character n-grams as features, it is fairly apparent that such an method will not to cowl a number of features (e.g.: semantics, discourse) required for the AES process. To unravel this drawback, we propose to combine string kernels with a current method primarily based on word embeddings, particularly the bag-of-tremendous-phrase-embeddings (BOSWE) Butnaru and Ionescu (2017). To our knowledge, that is the primary profitable try to mix string kernels and word embeddings. We consider our method on the Automated Student Assessment Prize information set, in both in-domain and cross-area settings. The empirical outcomes indicate that our strategy yields a greater performance than state-of-the-art approaches Phandi et al. 2015); Dong. Zhang (2016); Dong et al. 2017); Tay et al. String kernels. Kernel capabilities Shawe-Taylor and Cristianini (2004) capture the intuitive notion of similarity between objects in a particular domain. For example, in text mining, string kernels can be used to measure the pairwise similarity between text samples, merely based on character n-grams. Various string kernel capabilities have been proposed to date Lodhi et al. 2002); Shawe-Taylor. Cristianini (2004); Ionescu et al. 2014). One in every of the most recent string kernels is the histogram intersection string kernel (HISK) Ionescu et al. In our AES experiments, we use the intersection string kernel primarily based on a spread of character n-grams. SVR) Suykens and Vandewalle (1999); Shawe-Taylor and Cristianini (2004) for coaching. Bag-of-tremendous-phrase-embeddings. Word embeddings are long identified in the NLP community Bengio et al. 2003); Collobert and Weston (2008), but they’ve just lately grow to be extra standard because of the word2vec Mikolov et al. 2013) framework that enables the constructing of environment friendly vector representations from phrases. On top of the phrase embeddings, Butnaru and Ionescu (2017) developed an approach termed bag-of-tremendous-word-embeddings (BOSWE) by adapting an environment friendly computer imaginative and prescient technique, the bag-of-visual-words mannequin Csurka et al. 2004), for natural language processing tasks. The adaptation consists of replacing the image descriptors Lowe (2004) helpful for recognizing object patterns in pictures with phrase embeddings Mikolov et al. 2013) useful for recognizing semantic patterns in text paperwork. The BOSWE representation is computed as follows. First, each word in the gathering of coaching documents is represented as phrase vector utilizing a pre-educated word embeddings mannequin. Based on the fact that word embeddings carry semantic data by projecting semantically associated phrases in the identical area of the embedding house, the subsequent step is to cluster the word vectors so as to acquire relevant semantic clusters of phrases. As in the standard bag-of-visible-phrases mannequin, the clustering is done by k-means Leung and Malik (2001), and the formed centroids are stored in a randomized forest of okay-d bushes Philbin et al. 2007) to cut back search cost. The centroid of each cluster is interpreted as a brilliant word embedding or super word vector that embodies all of the semantically related word vectors in a small region of the embedding house. Every embedded phrase in the gathering of documents is then assigned to the closest cluster centroid (the nearest super word vector). Put collectively, the super phrase vectors generate a vocabulary (codebook) that can additional be used to explain every doc as a bag-of-tremendous-word-embeddings. To obtain the BOSWE represenation for a document, we just should compute the incidence depend of every tremendous phrase embedding in the respective doc. After constructing the representation, we make use of a kernel methodology to prepare the BOSWE mannequin for our specific job. POSTSUBSCRIPT. Kernel strategies work by embedding the information in a Hilbert house and by searching for linear relations in that area, using a learning algorithm. Therefore, we mix HISK and BOSWE within the twin (kernel) kind, by simply summing up the 2 corresponding kernel matrices. However, summing up kernel matrices is equivalent to function vector concatenation in the primal Hilbert area. POSTSUBSCRIPT. We can now combine HISK and BOSWE in two ways. POSTSUPERSCRIPT. Either way, the two approaches, HISK and BOSWE, are fused before the training stage. SVR, to seek out a greater regression function. The ASAP knowledge set comprises 8 prompts of different genres. The number of essays per immediate together with the rating ranges are presented in Table 1. For the reason that official check information of the ASAP competitors just isn’t launched to the public, we, as well as others earlier than us Phandi et al. 2015); Dong. Zhang (2016); Dong et al. 2017); Tay et al. 2018), use only the training information in our experiments. Evaluation procedure. As Dong and Zhang (2016), we scaled the essay scores into the vary 0-1. We carefully adopted the same settings for information preparation as Phandi et al. 2015); Dong. Zhang (2016). For the in-domain experiments, we use 5-fold cross-validation. The 5-fold cross-validation procedure is repeated for 10 occasions. The outcomes have been averaged to reduce the accuracy variation launched by randomly selecting the folds. For the cross-area experiments, we use the identical source→→ightarrow→target domain pairs as Phandi et al. 2015); Dong and Zhang (2016), particularly, 1→→ightarrow→2, 3→→ightarrow→4, 5→→ightarrow→6 and 7→→ightarrow→8. All essays in the source area are used as training knowledge. Target domain samples are randomly divided into 5 folds, where one fold is used as check information, and the opposite four folds are collected collectively to sub-pattern goal area prepare information. The sub-sampling is repeated for five times as in Phandi et al. 2015); Dong and Zhang (2016) to cut back bias. 0. As analysis metric, we use the quadratic weighted kappa (QWK). Baselines. We evaluate our method with state-of-the-artwork methods based mostly on handcrafted features Phandi et al. 2015), in addition to deep options Dong and Zhang (2016); Dong et al. 2017); Tay et al. 2018). We note that results for the cross-area setting are reported only in some of these recent works Phandi et al. Implementation selections. For the string kernels approach, we used the histogram intersection string kernel (HISK) based mostly on the blended range of character n-grams from 1 to 15. To compute the intersection string kernel, we used the open-source code supplied by Ionescu et al. 2014). For the BOSWE approach, we used the pre-educated word embeddings computed by the word2vec toolkit Mikolov et al. We used functions from the VLFeat library Vedaldi and Fulkerson (2008) for the other steps concerned within the BOSWE approach, such because the okay-means clustering and the randomized forest of k-d bushes. 500. POSTSUBSCRIPT-normalized intersection kernel. We mix HISK. BOSWE in the dual kind by summing up the two corresponding matrices. We mix HISK and BOSWE within the twin type by summing up the two corresponding matrices. POSTSUPERSCRIPT in all our experiments. In-domain outcomes. The outcomes for the in-domain automated essay scoring task are presented in Table 2. In our empirical research, we additionally include feature ablation outcomes. We report the QWK measure on every immediate as well as the overall common. 2015); Dong. Zhang (2016); Dong et al. 2017); Tay et al. 2017); Tay et al. 2018). After we combine the 2 models (HISK and BOSWE), we obtain even better results. Indeed, the mix of string kernels and phrase embeddings attains the perfect performance on 7 out of 8 prompts. 2017); Tay et al. Cross-domain outcomes. The outcomes for the cross-area automated essay scoring job are offered in Table 3. For each and every source→→ightarrow→target pair, we report higher results than each state-of-the-art strategies Phandi et al. 2015); Dong. Zhang (2016). We observe that the distinction between our greatest QWK scores. The opposite approaches are generally much increased within the cross-domain setting than in the in-area setting. We particularly discover that the difference from Phandi et al. Discussion. It’s worth noting that in a set of preliminary experiments (not included within the paper), we really thought of one other approach based mostly on phrase embeddings. We tried to obtain a doc embedding by averaging the phrase vectors for every document. SVR technique. Unfortunately, this isn’t possible because our strategy works in the twin house and we can not remodel the twin weights into primal weights, as long as the histogram intersection kernel does not have an express embedding map related to it. In future work, however, we intention to substitute the histogram intersection kernel with the presence bits kernel, which will allow us to perform an error analysis primarily based on the overused or underused patterns, as described by Ionescu et al. In this paper, we described an strategy based on combining string kernels and phrase embeddings for automatic essay scoring. We compared our approach on the Automated Student Assessment Prize knowledge set, in each in-area and cross-domain settings, with several state-of-the-artwork approaches Phandi et al. 2015); Dong. Zhang (2016); Dong et al. 2017); Tay et al. 2018). Overall, the in-area and the cross-area comparative studies indicate that string kernels, both alone and together with word embeddings, attain the very best performance on the automatic essay scoring activity. Using a shallow approach, we report better outcomes in comparison with latest deep learning approaches Dong and Zhang (2016); Dong et al. 2017); Tay et al. We thank the reviewers for his or her useful feedback.

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