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train_9200 | • Most of the individual features, including the asymmetric features (posSem and dirRel) did not perform well by themselves. | somewhat surprisingly, the graph-theoretic metric lexN W played a greater role by itself (Accuracy=0.7084), implying that this simple NW-based metric could capture some aspect of the directionality of evocation relationships. | contrasting |
train_9201 | The results reported thus far were achieved by utilizing Autoextend synset semantic vectors. | alternatives Table 7: Comparison of relational vector types. | contrasting |
train_9202 | The other extreme of the spectrum features resources compiled from user-generated content, such as micro-blogs. | these resources often suffer from grammar errors and misspellings, excessive use of acronyms and shortenings, partly due to the constrains of the publication means (e.g. | contrasting |
train_9203 | Surprisingly, we were only able to find subtitles for 12,618 movies and series. | since series are comprised of various episodes, we downloaded subtitles for each episode of every season available in OpenSubtitles. | contrasting |
train_9204 | A key challenge of text difficulty evaluation is that linguistic difficulty arises from both vocabulary and grammar (Richards and Schmidt, 2013). | most existing tools either do not sufficiently take the impact of grammatical difficulty into account (Smith III et al., 2014;Sheehan et al., 2014), or use traditional syntactic features, which differ from what language students actually learn, to estimate grammatical complexity (Schwarm and Ostendorf, 2005;Heilman et al., 2008;François and Fairon, 2012). | contrasting |
train_9205 | The relationship between text readability and reading devices was also studied in the past two years (Kim et al., 2014). | most of these approaches are intended for native speakers and use texts from daily news, economic journals or scientific publications, which are too hard to read for beginning and intermediate language learners. | contrasting |
train_9206 | L2R reaches 70.39%, 81.46%, 78.14%, and 55.54% of performances of the monolingual run in FR, DE, ES, and IT collections respectively. | the proposed L2R outperforms all the baselines with long queries in almost all the metrics. | contrasting |
train_9207 | TKs allow for using all the substructures of the relational structures as features in the learning algorithm. | in Web forums the TK performance is downgraded by the presence of noise and insignificant information, which also makes TKs too slow for processing large datasets. | contrasting |
train_9208 | In this work, we also tried to exploit neural models using their top-level representations for the (q o , q s ) pair and fed them into the TK classifier as proposed by , but this simple combination proved to be ineffective as well. | neural embeddings and weights can be useful for selecting better representations for TK models. | contrasting |
train_9209 | A Freebase predicate is a predefined relation, mostly consisting of a few words: "place of birth", "nationality", "author editor" etc. | a pattern is highly variable in length and word choice, i.e., the subsequence of the question that represents the predicate in a question can take many different forms. | contrasting |
train_9210 | (2015), we propose to apply dropout to the cell update vector g t as follows: Different from methods of (Moon et al., 2015;Gal, 2015), our approach does not require sampling of the dropout masks once for every training sequence. | as we will show in Section 4, networks trained with a dropout mask sampled per-step achieve results that are at least as good and often better than per-sequence sampling. | contrasting |
train_9211 | We first note that approximating Algorithm 1 by ignoring the rejection sampling step results in slightly worse performance. | without the rejection sampling, copLDA converges faster in terms of iterations. | contrasting |
train_9212 | Note that senLDA is the model with the fastest convergence rate with respect to the number of Gibbs iterations. | lDA, coplDA sen and coplDA np require the same number of iterations, which depends on the dataset. | contrasting |
train_9213 | In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. | state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. | contrasting |
train_9214 | The first interactive machine translation systems (Kay and Martins, 1973;Zajac, 1988;Yamron et al., 1993) focus on having human translators disambiguate the source texts through answering questions. | this question-answering process remains a laborious one for human translators. | contrasting |
train_9215 | Hunter and Resnik (2010) directly introduce the source-language syntactic constraints into the decoding of phrasebased MT system, which are almost the same as our work. | this method builds an independent syntactic re-ordering model and scores the hypotheses through features. | contrasting |
train_9216 | Interestingly, we also find that the topic-informed NMT system tends to produce more words in translations, namely 50,913 and 34,695 words in NIST 2004 andNIST 2005, respectively. | the NMT baseline produces 44,552 and 30,558 words in NIST 2004 andNIST 2005, respectively. | contrasting |
train_9217 | Inspecting the translation examples in Figure 5, 议长/speaker and 活跃/active fail to be translated by the baseline NMT system. | topic-informed NMT is able to use the known topic information, either from the source sentences or previous translations, to produce correct translations. | contrasting |
train_9218 | L m and L v are cross-lingual objectives which are defined as the dissimilarities between statistics: where i is index of each element in the vectors. | it is nontrivial to optimize the monolingual and cross-lingual objectives simultaneously in equation 7. | contrasting |
train_9219 | So the first approach to combine neural and statistical machine translation is not able the combine the strength of both system. | the NMT system seems to be not able to recover from the errors done by the SMT-based system. | contrasting |
train_9220 | For example, vectorial representations of the contexts are often used (Turney and Pantel, 2010), but unrelated with weighting schemes and relevance functions used in IR (with the exception of Vechtomova and Robertson (2012) in the slightly different context of computing similarities between named entities). | the weighting of contexts provides more relevant neighbors. | contrasting |
train_9221 | Moreover, we also use a least squares formulation to constrain these vectors such that w i •w j reflects the co-occurrence statistics of words i and j. | to GloVe, however, we explicitly model two factors that contribute to the residual error of this model: (i) the fact that corpus statistics for rare terms are not reliable and (ii) the fact that not all words are equally informative. | contrasting |
train_9222 | Moreover, one might presume that word associations are themselves simply derived from the distribution of words in the external language: in that case, one would expect them to be an inferior and noisy measure. | several strands of research support the idea that word associations capture representations that cannot be fully reduced to the distributional properties of the E-language environment. | contrasting |
train_9223 | This is contrasted with more statistical approaches such as Latent Semantic Analysis (Landauer and Dumais, 1997) and topic models (Griffiths et al., 2007). | it is not clear that this holds up in light of the fact that we find very little difference in performance between count models and word2vec, or previous work arguing that word embedding models perform an implicit matrix factorization (Levy and Goldberg, 2014). | contrasting |
train_9224 | When used independently, the performance is the same as always predicting the major category. | the string of the connective provides strong clues for the relation type. | contrasting |
train_9225 | The reason behind this is probably due to the fact that the existence of a connective often gives strong hint on at least one side of the interval. | there is often no explicit indication on the boundary of the other side of the interval. | contrasting |
train_9226 | Secondly, the arguments for a relation may be useful for relation type recognition. | the accuracy for argument extraction must be improved before the extracted arguments are used as features. | contrasting |
train_9227 | Moreover, factuality (Fact), aspect (Asp), modality (Mod) and polarity (Pol) information prove to be useful for discourse parsing. | the tasks derived from tense (Ten) and coreference (Coref) annotations do not lead to improvements. | contrasting |
train_9228 | Discourse parsing has proven useful for many applications (Taboada and Mann, 2006), ranging from summarization (Daumé III and Marcu, 2009;Thione et al., 2004;Sporleder and Lapata, 2005;Louis et al., 2010), sentiment analysis (Bhatia et al., 2015) or essay scoring (Burstein et al., 2003;Higgins et al., 2004). | the range of applications and the improvement allowed are for now limited by the low performance of the existing discourse parsers. | contrasting |
train_9229 | Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs. | conventional vector representations usually adopt embeddings at the word level and cannot well handle the rare word problem without carefully considering morphological information at character level. | contrasting |
train_9230 | Unlike the traditional model in which words usually represent as one-hot vectors and are independent with each other, vector space models reveal the relationship and capture the intuition among words which different or similar to others along a variety of dimensions (Mikolov et al., 2013). | embeddings considering only at word level is usually not good for rare words as discussed above and we introduce character-level embedding to enhance the current word embedding. | contrasting |
train_9231 | Previous work on non-cooperation has focused mainly on non-linguistic task-related non-cooperation or modelled non-cooperation in terms of special rules describing non-cooperative behaviours. | we start from rules for normal/correct dialogue behaviour-i.e., a dialogue game-which in principle can be derived from a corpus of cooperative dialogues, and provide a quantitative measure for the degree to which participants comply with these rules. | contrasting |
train_9232 | These contributions show that user-model approaches to dialogue modelling are flexible enough to account for situations of an arbitrary degree of intricacy. | as noted, e.g., by Taylor et al. | contrasting |
train_9233 | A recent version of the system (Plüss et al., 2011;Traum, 2012) supports cooperative, neutral and deceptive behaviour, and also is able to reason in terms of secrecy in order to avoid volunteering certain pieces of information. | their model the adversarial scenarios by means of a set of rules that the interlocutors follow. | contrasting |
train_9234 | No specific model of events is required here. | this representation is too simplistic to describe many of the temporal relations that are often explicitly conveyed in language. | contrasting |
train_9235 | When approached as a classification task, the assumption made by most tools is that classes are independent. | this is not correct in this case, for two reasons. | contrasting |
train_9236 | Folding offers a big improvement over other methods, in terms of both error reduction above most-common-class baseline and also absolute accuracy. | it is not applicable to real-world data. | contrasting |
train_9237 | The expressivity of a representation has an inverse correlation with automatic temporal relation typing performance. | more expressive representations are required in order to accurately capture temporal structure, or even to reduce annotator confusion. | contrasting |
train_9238 | An events in ECB+ can be in one of 3 categories: (b) Event Types Figure 2: (a) Documents in ECB+ are divided into topics and sub-topics, and coreference links can exist across documents in the same sub-topic. | a coreference system is not aware of the topic (or sub-topic) partition at test time. | contrasting |
train_9239 | As a result, a system can (incorrectly) link two event mentions across topics (or sub-topics), for which it should be appropriately penalized in evaluation. | we will see in the next section that current evaluations do not meet this requirement. | contrasting |
train_9240 | Note that this behavior is not exclusive to the SIMPLE-CDEC -a system which exclusively predicts within document links can achieve such high scores under the B&H and YCF settings. | we can get a clearer assessment of cross document coreference performance by evaluating the models under the PURE-CDEC setting. | contrasting |
train_9241 | It should be noted that Lemma performs worse than Lemma-WD because it makes incorrect cross document links, due to the naive nature of the lemma match. | lemma-WD does not make any across document links, avoiding incurring these penalties. | contrasting |
train_9242 | We believe that this claim held in these works only due to the lenient evaluation settings of B&H and YCF, which did not appropriately penalize the incorrect across topic (and sub-topic) links made by the Lemma baseline. | the SIMPLE-CDEC and PURE-CDEC evaluations show that Lemma-δ is a stronger baseline. | contrasting |
train_9243 | Our task differs from this task in that we aim to estimate the segment boundaries of unsegmented lyrics using machine learning techniques. | to the segmentation of lyrics, much previous work has analyzed and estimated the segment structure of music audio signals using repeated musical parts such as verse, bridge, and chorus (Foote, 2000;Lu et al., 2004;Goto, 2006;Paulus and Klapuri, 2006;McFee and Ellis, 2014). | contrasting |
train_9244 | tends to appear at the end of a segment. | a phrase like "I'm sorry" may appear at the beginning of a segment. | contrasting |
train_9245 | This result supports the hypothesis that sequences of repeated lines (diagonal lines in the SSM) are important clues for modeling lyrics segmentation. | to results reported in text segmentation literature (Beeferman et al., 1999), TF features turned out to be ineffective for lyrics segment boundary estimation, except for TF2 unigram features. | contrasting |
train_9246 | More investigation is needed for further improvement. | to the case of Figure 5, Figure 6 shows a typical example of false negatives. | contrasting |
train_9247 | Reithinger and Klesen (1997) used a language model to predict the probability of a certain DA. | the effort to predict probability using a language model results in a severe loss of information, thereby leading to a poor result. | contrasting |
train_9248 | The audio feature based Western music mood classification system achieved better F-measure (11.5 points absolute, 19.5% relative) than the Hindi-one. | there were less number of instances present in the Western music as compared to the Hindi music. | contrasting |
train_9249 | Therefore, both SVM (classification) and SVM (regression) failed on this case. | lSTM models term dependencies in sequences with a memorizing-forgetting mechanism. | contrasting |
train_9250 | (Boyer et al., 2010) used acts in conjunction with more specific task actions, e.g., opening a specific file, etc., to discover hidden modes using a HMM. | we assume a pre-defined set of modes (see next section) that generalize across tutors and identify modes and acts jointly. | contrasting |
train_9251 | Recall that in MLNs, generally, all the groundings of a first-order formula share the exact same weight. | in several practical cases, we need to decrease the bias of the model by introducing more parameters for it. | contrasting |
train_9252 | In our experiments, we use Gurobi, a state-of-the-art ILP solver to compute the MAP solution for the MLN (Sarkhel et al., 2014). | it turns out that a naive application of approximate MAP solvers to our problem is still infeasible in practice. | contrasting |
train_9253 | Recently, a statistical based approach has been proposed for resolving NSU question (Raghu et al., 2015). | this approach only focuses on the simpler problem of (a) how old was john rolfe when he died ? | contrasting |
train_9254 | RNN encoder decoder models have been successfully trained on huge parallel corpus of millions of sentences Sutskever et al., 2014). | it is extremely hard to obtain conversation data of this magnitude. | contrasting |
train_9255 | A standard RNN encoder decoder model will end up having UNK symbols as output. | it is not possible to determine which word does this symbol correspond to, as there will be typically many UNK words in an input sequence. | contrasting |
train_9256 | One possible method to evaluate our models is to manually compare the generated output sequence to gold standard (collected from a held out set). | this method is slow, human intensive and prone to errors. | contrasting |
train_9257 | (2015) propose a taskoriented NLG model that can generate the responses providing the correct answers given the dialogue act (for instance, confirm or request some information), including the answer information. | the context information, such as the input question and dialogue act, is ignored. | contrasting |
train_9258 | To address the problem, Oh and Rudnicky 2000propose a statistical approach which can learn from data to generate variant language using a class-based n-gram language model. | due to the limits of the model and the lack of semantically-annotated corpora, the statistical approach cannot ensure the adequacy and readability of the generated language. | contrasting |
train_9259 | For ease of notation, we assume here without loss of generality that every word is modelled with a single-state Hidden Markov Model (HMM); in fact, every word is actually composed of a sequence of phonemes, and every phoneme is modelled by an HMM with 3 emitting states. | this hierarchy of models would lead to excessively long equations, and we prefer to simplify the presentation of this baseline. | contrasting |
train_9260 | In general, these methods added resource-based knowledge to their systems in order to form word vector representations, showing impressive performance gains over methods which did not address the rare words problem. | soricut and Och (2015) applied an automatic method to induce morphological rules and transformations as vectors in the same embedding space. | contrasting |
train_9261 | In comparison to LSTM and CCNN-LSTM, LBL2 SW ordSS 's lower performance on test data was expected as the former are more non-linearly complex language models. | for tasks like spoken term detection, having low perplexities on most frequent set of words is not good enough and hence, we compare LMs on the perplexity of a rare-word based test set. | contrasting |
train_9262 | Access to the displayed snippets is on demand and the user can access the information in context without the need to formulate a specific query. | these advantages are fundamentally based on how well the system is able to retrieve relevant documents, as the system's utility diminishes when proposing a lot of irrelevant documents. | contrasting |
train_9263 | they are all pretrained on the same Simple English Wikipedia dump from May 2016. | our proposed method and the TF-IDF baseline can also produce terms that are DRUID multiwords, whereas the original implementation of Habibi and Popescu-Belis (2015) can only produce single keywords. | contrasting |
train_9264 | We also saw good results using the method proposed by Habibi and Popescu-Belis (2015), with the diversity constraint (λ) set to the default value of 0.75, which was the optimal value in the domain of meeting transcripts. | we noticed that the publicly available Matlab implementation of this method 18 only removed stopwords as part of its preprocessing (5). | contrasting |
train_9265 | "in coal power plant" appears in the transcript instead of "nuclear power plant". | in this example Habibi and Popescu-Belis' system finds better matching articles, like "Microscope", "Light Microscope", "Mangrove" , "Fresh water", "River delta", "Fishing" which can be attributed to finding the keyword "microscope" and otherwise picking simpler keywords like "ocean", "sea", "fishing" and "river", which our system entirely misses. | contrasting |
train_9266 | This can change the dense vectors and IDF values for the constituents of multiwords compared to training on single words and thus affect ranking scores. | in some of the automatic transcriptions, only constituents of the correct multiwords can be found because of transcription errors, so that our method has to rank the constituent instead of the full multiword. | contrasting |
train_9267 | In previous work, NLP methods have been successfully applied to both assessing proficiency levels in L2 input texts collected from coursebooks and output texts written by learners (see section 2). | the two text types have always been considered separately, while we argue that there is a shared linguistic content between the two that can be used for knowledge transfer. | contrasting |
train_9268 | Looking into POS diversity and parser label diversity as shown in Table 4: Accuracy from 10-folds cross validation using all features diversity; 0.37 for syntax labels diversity). | this feature may correlate with review length, which is considerably higher for experts (362 words) than for laymen (107 words). | contrasting |
train_9269 | (2014) show that structured events from open information extraction (Yates et al., 2007;Fader et al., 2011) can achieve better performance compared to conventional features, as they can capture structured relations. | one disadvantage of structured representations of events is that they lead to increased sparsity, which potentially limits the predictive power. | contrasting |
train_9270 | Second, we use a simpler neural tensor network model to learn knowledge graph embedding, which is easier to train. | the baseline event embedding model uses a recursive neural tensor network architecture to preserve the original structure of events. | contrasting |
train_9271 | This is because it is difficult to investigate the relationship among companies, and therefore news about other companies can be noise for predicting the stock prices of a company. | knowledge graph can provide attributes of entities and relations between them, hence it is possible to learn more information from related companies to help decide the direction of individual stock price movements. | contrasting |
train_9272 | Topic modeling techniques based on Dirichlet allocation are recently popular in text analytics (Blei et al., 2003), which is particularly effective on long documents to find meaningful topics (Blei et al., 2004). | we hereby emphasize that all these techniques are not directly helpful to improve recommendation systems. | contrasting |
train_9273 | Others, staying closer to the surface level, have recently experimented with sophisticated measures of textual similarity (Jimenez et al., 2013;Sultan et al., 2016) or inferring informative answer patterns (Ramachandran et al., 2015). | similar to other NLP tasks (like, for example, TE), one of the biggest challenges remains beating the lexical baseline: At SemEval-2013, the baseline consisting of textual similarity measures comparing reference and student answer frequently was not outperformed. | contrasting |
train_9274 | These corpora contain answers by mostly highly proficient speakers. | there are learner corpora assessing language students' reading comprehension by asking questions about the content of a text. | contrasting |
train_9275 | 4 The results are better on the aggression-loss subset (BCS) than on the full dataset (TCF). | the aggression-loss subset does not represent real-world data, as all tweets that are not labeled loss or aggression were removed prior to the experiment. | contrasting |
train_9276 | From the statistics, we observe that the products "Samsung Galaxy" and "Xbox" are actively discussed by the users with the 39%, 37% active level respectively. | the communication on the topic "PlayStation" is not as frequent as the communication on the other two topics. | contrasting |
train_9277 | (Hu and Liu, 2004;Pang and Lee, 2008;Mukherjee et al., 2012). | all these methods fail to explore the reason why people express or change their opinions. | contrasting |
train_9278 | The results show that the positively influential users more likely utilize the words describing the facts, e.g., "security", "special" and impress". | the tweets posted by strong negative influential users are more emotional with the words like "Woo", "Wow" or the emoticons "o o". | contrasting |
train_9279 | Supervised scheme iqf • qf • icf also performs better than standard LDA at most of cases. | tf-idf-LDA gets the worst results in both datasets. | contrasting |
train_9280 | Superficially, noun synset relation structures seem largely to correspond, with hyponymy forming the backbone of a relation network. | on a closer look, various contrasts come to the fore. | contrasting |
train_9281 | However, we do not consider this approach further given the target domain and only mention it here for completeness. | as outlined above, the very nature of our problem invalidates the first line of approach. | contrasting |
train_9282 | Note that such methods assume that an exhaustive language specific list of MWEs is available. | since our task is primarily concerned with MWE "discovery", such standard lexicons may not be used. | contrasting |
train_9283 | 3 System description & algorithms Thus, so far we have established that the nature and size of tweets are an hindrance for the standard tokenization process. | using word graphs would circumvent both problems. | contrasting |
train_9284 | The trigger annotation schemes are similar, the two data sets share many event types, and events occur only intrasentential. | some event types in TAC are more fine-grained, e.g., TRANSPORT in ACE was split into TRANSPORT-PERSON and TRANSPORT-ARTIFACT in TAC. | contrasting |
train_9285 | Comparing full inference, we note that Yang and Mitchell (2016) use a pipeline approach: They first tag sentences for potential entity mention and event trigger candidates, and apply label inference afterwards. | we model both jointly. | contrasting |
train_9286 | For example, if an entity is the Target of an ATTACK event, it rarely becomes the Attacker, at least not within one document. | the same entity may be the Victim of a DIE event, but never the Agent. | contrasting |
train_9287 | Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. | memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. | contrasting |
train_9288 | The main merits of these representation learning based methods are that they do not rely on any linguistic tools and can be applied to different languages or domains. | the memory of these methods, such as Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and Gated Recurrent Unit (GRU) (Cho et al., 2014) compressing all the external sentences into a fixed-length vector, is typically too small to accurately remember facts from the past, and may lose important details for response generation (Shang et al., 2015). | contrasting |
train_9289 | Taxonomy generation and evaluation in this submission is restricted to English Wikipedia. | it can be easily adapted to other languages by porting the heuristics, a fairly straightforward task if a dependency parser is available in the target language. | contrasting |
train_9290 | Compared to the WIBI taxonomies, HEADS shows significantly lower precision and recall scores in this evaluation. | the losses can be largely attributed to the simplification procedure (cf. | contrasting |
train_9291 | Leveraging a dependency parser is a possible way to capture more complex aspect structures, or at least to provide clues about the presence of entity modifiers (e.g., via AMOD relations). | deep parsing is known to be inaccurate on NUT, and training suitable models difficult, since NUT lacks proper grammatical structure in the first place. | contrasting |
train_9292 | We use standard recall, precision, and F 1 score metrics. | due to the different granularity of the output produced by the systems and of the GS annotations, the definition of a correct extraction varies slightly with each evaluation task. | contrasting |
train_9293 | The full model significantly outperforms all baselines. | it does not match the handcrafted approach. | contrasting |
train_9294 | For example, although the Wikipedia article 2 "The geometry and topology of three-manifolds" is filed under the categories of interest "Hyperbolic geometry", "3-manifolds" and "Kleinian groups", the title as a whole does not represent a single concept. | the technical term "Riemannian manifold" would completely match the title of the Wikipedia 3 (or Encyclopedia of Math) article for the concept and would thus be identified as a type by our method. | contrasting |
train_9295 | On one hand, the performance of Types2X suggests that information coming from the types occurring in the queries alone may not be enough to produce significant improvements in retrieval efficiency. | our experiments have shown that it is only the combination of query expansion with type information (rather than with simple terms) that yields significant performance gains on these difficult queries. | contrasting |
train_9296 | The types "iwasawa decomposition" and "cartan decomposition" in the expanded set strongly relate to the types in the query (both are related to Levi decomposition of Lie algebras). | there are also some instances of weak association between query and expanded types. | contrasting |
train_9297 | To be successful, in addition to accuracy, real-world systems must be scalable and select the most accurate answer sentences in a short amount of time. | accuracy and speed/scalability are competing forces that often counteract each other. | contrasting |
train_9298 | For example, models based on neural networks have become very popular due to their strong accuracy for this task (Yu et al., 2014). | they are typically slower at training and test time as compared to simple models, which may limit their use on very large datasets (Joulin et al., 2016). | contrasting |
train_9299 | We used the dependency relations of words to alleviate this problem and thus improved the performance. | we still need to find more effective methods to overcome Korean linguistic barriers such as the free word order and the lack of language resources; and this problem will be studied in future research. | contrasting |
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