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train_2700 | The smatch score is the maximum of the f-scores. | for AMRs that contain large number of variables, it is not efficient to get the f-score by simply using the method above. | contrasting |
train_2701 | We present a pilot evaluation DM.HR on a standard task from distributional semantics, namely synonym choice. | to tasks like predicting word similarity We use the dataset created by Karan et al. | contrasting |
train_2702 | To identify news stories on the same topic, most TDT approaches rely on traditional vector space models (Salton et al., 1975), as more sophisticated natural language processing techniques have not yet proven to be useful for this task. | significant advances in sentence-level event extraction have been made over the last decade, in particular as the result of standardization efforts such as TimeML (Pustejovsky et al., 2003a) and TimeBank (Pustejovsky et al., 2003b), as well as dedicated evaluation tasks (ACE, 2005;Verhagen et al., 2007;Verhagen et al., 2010). | contrasting |
train_2703 | The VSM is at the core of most approaches that identify sametopic news stories (Hatzivassiloglou et al., 2000;Brants et al., 2003;Kumaran and Allan, 2005;Atkinson and Van der Goot, 2009). | it has been observed that some word classes (e.g., named entities, noun phrases, collocations) have more significance than the others. | contrasting |
train_2704 | When considering average precision, all kernel models significantly (at p < 0.01) outperform the baseline. | when considering R-precision, only the conormal PGK model significantly (at p < 0.05) outperforms the baseline. | contrasting |
train_2705 | In contrast, the system produced precision of 0.96 when ambiguity detection was employed. | the inclusion of disambiguation did reduce the overall recall; the system that employed disambiguation returned only about 57% of the true positives returned by the system that did not employ disambiguation. | contrasting |
train_2706 | Finally a multi-class classifier is trained. | the accuracy of DS is not satisfying. | contrasting |
train_2707 | (2012) design a generative model to identify noise patterns. | as shown in the experiments (Section 4), the above variants do not lead to much improvement in accuracy. | contrasting |
train_2708 | To reduce the number of false negative examples, we propose a new method to construct negative examples by utilizing the 1-to-1/1-to-n/n-to-1/n-to-n property of a relation. | 1-to-1/n-to-1/1-to-n Relation A 1-to-1 or n-to-1 relation is a functional relation: for a relation r, for each valid source entity e 1 , there is only one unique destination entity e 2 such that (e 1 , e 2 ) ∈ r. in a real KB like Freebase, very few relations meet the exact criterion. | contrasting |
train_2709 | Recall that in our second baseline, we employ ACs to postprocess the output of the stance classifier simply by summing up the confidence values assigned to the posts written by the same author for the same debate domain. | since we now want to enforce two types of inter-post constraints (namely, ACs and ICs), we will have to employ a more sophisticated inference mechanism. | contrasting |
train_2710 | Previous work has focused on employing graph minimum cut (MinCut) as the inference algorithm. | since MinCut suffers from the weakness of not being able to enforce negative constraints (i.e., two posts cannot receive the same label) (Bansal et al., 2008), we propose to use integer linear programming (ILP) as the underlying inference mechanism. | contrasting |
train_2711 | What's more these works all model on a word level. | it is very useful to regard sentence as the basic processing unit, for example in the text scanning approach simulating human reading process by Xu and Zhuge (2013). | contrasting |
train_2712 | In our task, we provide a set of potential events. | most of the candidate events won't have ever been reported within a user's posting history. | contrasting |
train_2713 | Finally, since correlations between the textual features and non-textual features are highly non-linear, concatenating these features simply sometimes can submerge classification performance. | mDBN enjoys the advantage of the shared repre-sentation between textual features and non-textual features using the deep learning architecture. | contrasting |
train_2714 | In (Hassan and Radev, 2010) and (Hassan et al., 2011), a Markov random walk model is applied to a large word relatedness graph, constructed according to the synonyms and hypernyms in WordNet (Miller, 1995). | approaches based on seed words has obvious shortcomings. | contrasting |
train_2715 | Tree kernels were inspired in part by ideas from Data-Oriented Parsing (Scha, 1990;Bod, 1993), which was in turn motivated by uncertainty about which fragments to include in a grammar. | manual and automatic approaches to inducing tree fragments have recently been found to be useful in an explicit approach to text classification, which employs specific tree fragments as features in standard classifiers (Post, 2011;Wong and Dras, 2011;Swanson and Charniak, 2012). | contrasting |
train_2716 | 1996;van de Weijer 1998) Table 1: Diphone behavior rich source of information for word segmentation: obstruent-initial diphones are generally informative as to the presence/absence of word boundaries. | as we suspected, vowelvowel sequences are problematic, since they occur freely both within words and across word boundaries. | contrasting |
train_2717 | Van Petten and Luka (2012) argue that word expectations that are confirmed result in reduced N400 size, whereas expectations that are disconfirmed increase the PNP. | in a probabilistic setting, expectations are not all-or-nothing so there is no strict distinction between confirmation and disconfirmation. | contrasting |
train_2718 | This has the advantage that our findings are likely to generalise to other sentence stimuli, but it can also raise a possible concern: The N400 effect may not be due to surprisal itself, but to an unknown confounding variable that was not included in the regression analysis. | this seems unlikely because of two additional findings that only follow naturally if surprisal is indeed the relevant predictor: Significant results only appeared where they were most expected a priori (i.e., on N400 but not on other components) and there was a nearly monotonic relation between the models' word-prediction accuracy and their ability to account for N400 size. | contrasting |
train_2719 | Ensemble methods are widely used in machine learning and have been shown to be often very effective (Breiman, 1996;Smyth and Wolpert, 1999;MacKay, 1991;Freund et al., 2004). | ensemble methods and their theory have been developed primarily for binary classification or regression tasks. | contrasting |
train_2720 | As the bottom-up Eisner Algorithm must maintain the nested structural constraint, it cannot parse the non-projective dependency trees like 8' and 9' in Figure 2. | the non-projective dependency does exist in real discourse. | contrasting |
train_2721 | A majority of researches regard discourse parsing as a classification task and mainly focus on exploiting various linguistic features and classifiers when using PDTB (Wellner et al., 2006;Pitler et al., 2009;Wang et al., 2010). | the predicatearguments annotation scheme itself has such a limitation that one can only obtain the local discourse relations without knowing the rich context. | contrasting |
train_2722 | As expected, the MT system slightly outperforms our models on most language pairs. | the overall performance of the models is comparable to that of the MT system. | contrasting |
train_2723 | Because our crawling algorithm so closely models the guidelines, this puts our system in an interesting position to provide feedback to the Shared Task organizers. | the close match between our crawling algorithm and the annotation guidelines supported by the mapping to MRS provides for very high precision and recall when the analysis engine produces the desired MRS. 7 the analysis engine does not always provide the desired analysis, largely because of idiosyncrasies of the genre (e.g. | contrasting |
train_2724 | Being rule-based, our system does not require any training data per se. | the majority of our rule development and error analysis were performed against the designated training data. | contrasting |
train_2725 | It lowers precisions in RTE2 and RTE3 data, particularly in "IE" subtask (where precisions drop under 0.5). | it occurs less often in "IR" subtask. | contrasting |
train_2726 | Furthermore, this method can only account for a very small part of phrases, since most of the phrases are compositional. | our method attempts to learn the semantic vector representation for any phrase. | contrasting |
train_2727 | Most of them also assume that the input must be in document level. | this situation does not always happen since there is considerable amount of parallel data which does not have document boundaries. | contrasting |
train_2728 | Analysing and extracting useful information from the web has become an increasingly important research direction for the NLP community, where many tasks require part-of-speech (POS) tagging as a fundamental preprocessing step. | state-of-the-art POS taggers in the literature (Collins, 2002;Shen et al., 2007) are mainly optimized on the the Penn Treebank (PTB), and when shifted to web data, tagging accuracies drop significantly (Petrov and McDonald, 2012). | contrasting |
train_2729 | We can see that using the target domain data achieves similar improvements compared with using the mixed data. | for the email domain, RBM-W yields much smaller improvement compared with RBM-E, and vice versa. | contrasting |
train_2730 | Being specific to Apple forums, we did not use them for initialization in experiments so far with the intent of keeping the technique generic. | when such posts are initialized as solutions (in addition to first replies as we did earlier), the F-score for solution identification for our technique was seen to improve slightly, to 64.5% (from 64%). | contrasting |
train_2731 | As for unigram features, not surprisingly, "rt" and "retweet" are top features for both our approach and TAC+ff+time. | the other unigrams for the two methods seem to be a bit different in spirit. | contrasting |
train_2732 | The convergence rate for the average posterior probability estimates P µ (R|T ) depending on the number of tweets is similar to the user model results presented in Figure 6. | for G geo the variance for P µ (R|T ) is higher for Democratic users; for G ZLR P µ (R|T ) → 1 for Republicans in less than 110 tweets which is ∆t = 40 tweets faster than the user model; for G cand the convergence for both P µ (R|T ) → 1 and P µ (D|T ) → 0 is not significantly different than the user model. | contrasting |
train_2733 | Much of the recent work on dependency parsing has been focused on solving inherent combinatorial problems associated with rich scoring functions. | we demonstrate that highly expressive scoring functions can be used with substantially simpler inference procedures. | contrasting |
train_2734 | Note that Equation 4 contains exponentially many constraints and cannot be enforced jointly for general scoring functions. | our sampling procedure generates a small number of structures along the search path. | contrasting |
train_2735 | Generally, this feature can be defined based on an instance of grandparent structure. | we also handle the case of coordination. | contrasting |
train_2736 | We apply the Random Walk-based sampling method (see Section 3.2.2) for the standard dependency parsing task. | for the joint parsing and POS correction on the CATiB dataset we do not use the Random Walk method because the first-order features in normal parsing are no longer first-order when POS tags are also variables. | contrasting |
train_2737 | On the CATiB dataset, we restrict the sample trees to always be projective as described in Section 3.2.1. | we do not impose this constraint for the CoNLL datasets. | contrasting |
train_2738 | Given sufficient time, both sampling methods achieve the same score. | the Random Walk-based sampler performs better when the quality is traded for speed. | contrasting |
train_2739 | Without the coarse pass, the dense marginal computation is not efficient on a GPU, processing only 32 sentences per second. | our approach allows us to process over 190 sentences per second, almost a 6x speedup. | contrasting |
train_2740 | the code itself), which allows the compiler to more effectively use all of its registers. | register space is limited on GPUs. | contrasting |
train_2741 | As Petrov and Klein (2007) have shown, intermediate-sized Berkeley grammars prune many more symbols than the X-bar system. | they are slower to parse with in a CPU context, and so they begin with an X-bar grammar. | contrasting |
train_2742 | The Viterbi algorithm is a reasonably effective method for parsing. | many authors have noted that parsers benefit substantially from minimum Bayes risk decoding (Goodman, 1996;Simaan, 2003;Matsuzaki et al., 2005;Titov and Henderson, 2006;Petrov and Klein, 2007). | contrasting |
train_2743 | By itself, this approach works on nearly every sentence. | scores for approximately 0.5% of sentences overflow (sic). | contrasting |
train_2744 | For any subsequent SHIFT action (SHIFT, c) to be valid, the necessary condition is c ≡ c lex 0 , where c lex 0 denotes the gold-standard lexical category of the front word in the queue, q 0 (line 3). | this condition is not sufficient; a counterexample is the case where all the goldstandard lexical categories for the sentence in Figure 2 are shifted in succession. | contrasting |
train_2745 | (2013d) compare their predict models to "Latent Semantic Analysis" (LSA) count vectors on syntactic and semantic analogy tasks, finding that the predict models are highly superior. | they provide very little details about the LSA count vectors they use. | contrasting |
train_2746 | Count models have such a long and rich history that we can only explore a small subset of the counting, weighting and compressing methods proposed in the literature. | it is worth pointing out that the evaluated parameter subset encompasses settings (narrow context window, positive PMI, SVD reduction) that have been found to be most effective in the systematic explorations of the parameter space conducted by Bullinaria and Levy (2007;. | contrasting |
train_2747 | Instead, we found that the predict models are so good that, while the triumphalist overtones still sound excessive, there are very good reasons to switch to the new architecture. | due to space limitations we have only focused here on quantitative measures: It remains to be seen whether the two types of models are complementary in the errors they make, in which case combined models could be an interesting avenue for further work. | contrasting |
train_2748 | results in performance which is almost 70% of ALLEQ, demonstrating the value of weakly supervised data. | 5EQ, which cannot use this weak supervision, performs much worse. | contrasting |
train_2749 | These systems are effective because researchers can incorporate a large body of handcrafted features into the models. | the ability of these models is restricted by the design of features and the number of features could be so large that the result models are too large for practical use and prone to overfit on training corpus. | contrasting |
train_2750 | In addition, (Hai et al., 2012) extracted opinion targets/words in a bootstrapping process, which had an error propagation problem. | we perform extraction with a global graph co-ranking process, where error propagation can be effectively alleviated. | contrasting |
train_2751 | Semi-supervised techniques have been proposed for sentence-level sentiment classification (Täckström and McDonald, 2011a;Qu et al., 2012). | they rely on a large amount of document-level sentiment labels that may not be naturally available in many domains. | contrasting |
train_2752 | Most previous work using PR mainly experiments with featurelabel constraints. | we explore a rich set of linguistically-motivated constraints which cannot be naturally formulated in the feature-label form. | contrasting |
train_2753 | CRF-INF disc slightly outperforms CRF but the improvement is not significant. | both PR lex and PR significantly outperform CRF, which implies that incorporating lexical and discourse constraints as posterior constraints is much more effective. | contrasting |
train_2754 | This is because it over-predicts the polar sentences in the polar documents, and predicts no polar sentences in the neutral documents. | our PR models provide more balanced F1 scores among all the sentiment categories. | contrasting |
train_2755 | A simple lexicon-based constraint during inference time may also correct this case. | hard-constraint baselines can hardly improve the performance in general because the contributions of different constraints are not learned and their combination may not lead to better predictions. | contrasting |
train_2756 | The second example in Table 5 shows that the PR model learned with discourse constraints correctly predicts the sentiment of two sentences where no lexical constraints apply. | discourse constraints are not always helpful. | contrasting |
train_2757 | Previous works often use syntax constituents in this task. | syntax-based methods can only use discrete contextual information, which may suffer from data sparsity. | contrasting |
train_2758 | A recent research (Xu et al., 2013) extracted infrequent product features by a semi-supervised classifier, which used word-syntactic pattern co-occurrence statistics as features for the classifier. | this kind of feature is still sparse for infrequent candidates. | contrasting |
train_2759 | In conventional neural models, the candidate term t is placed in the center of the window. | from Example 2, when l = 5, we can see that the best windows should be the bracketed texts (Because, intuitively, the windows should contain mp3, which is a strong evidence for finding the product feature), where t = {screen} is at the boundary. | contrasting |
train_2760 | We believe the reason that LEX or CONT is better is that syntactic patterns only use discrete and local information. | cONT exploits latent semantics of each word in context, and LEX takes advantage of word embedding, which is induced from global word co-occurrence statistic. | contrasting |
train_2761 | As for SGW-TSVM, the features they used for the TSVM suffer from the data sparsity problem for infrequent terms. | lEX&CONT is frequency-independent to the review corpus. | contrasting |
train_2762 | Topic modeling is a popular method for the task. | unsupervised topic models often generate incoherent aspects. | contrasting |
train_2763 | In (Yang et al., 2011), a user provided parameter indicating the technicality degree of a domain was used to model the language gap between topics. | our method is fully automatic without human intervention. | contrasting |
train_2764 | The terms in each document are assumed to be generated by first sampling a topic z, and then a cluster c given topic z, and finally a term w given topic z and cluster c. This plate notation of AKL and its associated generative process are similar to those of MC-LDA (Chen et al., 2013b). | there are three key differences. | contrasting |
train_2765 | Traditionally, topic models have been evaluated using perplexity. | perplexity on the heldout test set does not reflect the semantic coherence of topics and may be contrary to human judgments (Chang et al., 2009). | contrasting |
train_2766 | Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expectation maximization. | these new methods lack the rich priors associated with probabilistic models. | contrasting |
train_2767 | (2012a) chose the C that minimizes the KL divergence betweenQ i,• and the reconstruction based on the anchor word's conditional word vec- (3) The anchor method is fast, as it only depends on the size of the vocabulary once the cooccurrence statistics Q are obtained. | it does not support rich priors for topic models, while MCMC (Griffiths and Steyvers, 2004) and variational EM (Blei et al., 2003) methods can. | contrasting |
train_2768 | This paper introduces two different regulariza-tions that offer users more interpretable models and the ability to inject prior knowledge without sacrificing the speed and generalizability of the underlying approach. | one sacrifice that this approach does make is the beautiful theoretical guarantees of previous work. | contrasting |
train_2769 | The latter have been applied specifically to the problem of estimating word probabilities with sparse additive generative (SAGE) models (Eisenstein et al., 2011), where sparse extra-linguistic effects can influence a word probability in a larger generative setting. | to previous work in which the probability of a word linked to a character is dependent entirely on the character's latent persona, in our model, we see the probability of a word as dependent on: (i) the background likelihood of the word, (ii) the author, so that a word becomes more probable if a particular author tends to use it more, and (iii) the character's persona, so that a word is more probable if appearing with a particular persona. | contrasting |
train_2770 | Then we define the weight matrix representing the coreferential relation as: if m i and m j are coreferential, and c i = c j 0 Otherwise Ensuring topical coherence (Principle 3) has been beneficial for wikification on formal texts (e.g., News) by linking a set of semantically-related mentions to a set of semantically-related concepts simultaneously Ratinov et al., 2011;Cheng and Roth, 2013). | the shortness of a single tweet means that it may not provide enough topical clues. | contrasting |
train_2771 | We can easily see that the system performce is stable when µ < 0.4. | when µ ≥ 0.4, the system performance dramatically decreases, showing that prior popularity is not enough for an end-toend wikification system. | contrasting |
train_2772 | The text of any DOM tree node that is shorter than 140 characters is a candidate entity. | without further restrictions, the number of possible entity lists grows exponentially with the number of candidate entities. | contrasting |
train_2773 | In the literature, many information extraction systems employ more versatile extraction predicates (Wang and Cohen, 2009;Fumarola et al., 2011). | despite the simplicity, we are able to find an extraction predicate that extracts a compatible entity list in 69.7% of the development examples. | contrasting |
train_2774 | For instance, entities in many categories (e.g., people and place names) usually have only 2-3 word tokens, most of which are proper nouns. | random words on the web page tend to have more diverse lengths and part-of-speech tags. | contrasting |
train_2775 | Since zero-shot entity extraction is a new task, we cannot directly compare our system with other systems. | we can mimic the settings of other tasks. | contrasting |
train_2776 | Our work shares a base with the wrapper induction literature (Kushmerick, 1997) in that it leverages regularities of web page structures. | wrapper induction usually focuses on a small set of web domains, where the web pages in each domain follow a fixed template (Muslea et al., 2001;Crescenzi et al., 2001;Cohen et al., 2002;Arasu and Garcia-Molina, 2003). | contrasting |
train_2777 | In recent years, there has been a drive to scale semantic parsing to large databases such as Freebase (Cai and Yates, 2013;Berant et al., 2013;Kwiatkowski et al., 2013). | despite the best efforts of information extraction, such databases will always lag behind the open web. | contrasting |
train_2778 | For example, a PER mention is unlikely to have more than one employer. | a GPE mention can be a physical location for multiple entity mentions. | contrasting |
train_2779 | All these work noted the advantage of exploiting crosscomponent interactions and richer knowledge. | they relied on models separately learned for each subtask. | contrasting |
train_2780 | Using parse accuracy in a simple reranking strategy for selfmonitoring, we find that with a stateof-the-art averaged perceptron realization ranking model, BLEU scores cannot be improved with any of the well-known Treebank parsers we tested, since these parsers too often make errors that human readers would be unlikely to make. | by using an SVM ranker to combine the realizer's model score together with features from multiple parsers, including ones designed to make the ranker more robust to parsing mistakes, we show that significant increases in BLEU scores can be achieved. | contrasting |
train_2781 | Simple ranking with the Berkeley parser of the generative model's n-best realizations raised the BLEU score from 85.55 to 86.07, well below the averaged perceptron model's BLEU score of 87.93. | as shown in Table 2, none of the parsers yielded significant improvements on the top of the perceptron model. | contrasting |
train_2782 | As with the base grammar, missing grammar entries are guessed from the expanded grammar. | we do this only in cases where a correct grammar entry cannot be guessed from the base grammar. | contrasting |
train_2783 | Conceptually, this conversion is similar to the conversions from CTB structures to representations in deep grammar formalisms (Tse and Curran, 2010;Yu et al., 2010;Guo et al., 2007;Xia, 2001). | our work is grounded in GB, which is the linguistic basis of the construction of CTB. | contrasting |
train_2784 | Supervised dependency parsing has made great progress during the past decade. | it is very difficult to further improve performance * Correspondence author of supervised parsers. | contrasting |
train_2785 | To alleviate the noise, the tri-training method only uses unlabeled data on which multiple parsers from different views produce identical parse trees. | unlabeled data with divergent syntactic structures should be more useful. | contrasting |
train_2786 | When the parse forests of the unlabeled data are the union of the outputs of GParser and ZPar, denoted as "Unlabeled ← Z+G", each word has 1.053 candidate heads on English and 1.136 on Chinese, and the oracle accuracy is higher than using 1-best outputs of single parsers (94.97% vs. 92.85% on English, 86.66% vs. 82.46% on Chinese). | we find that although the parser significantly outperforms the supervised GParser on English, it does not gain significant improvement over co-training with ZPar ("Unlabeled ← Z") on both English and Chinese. | contrasting |
train_2787 | This space then reflects the "semantic ground truth" of shared lexical meanings in a language community's vocabulary. | corpus-based VSMs have been criticized as being noisy or incomplete representations of meaning (Glenberg and Robertson, 2000). | contrasting |
train_2788 | 1 If brain activation data encodes semantics, we theorized that including brain data in a model of semantics could result in a model more consistent with semantic ground truth. | the inclusion of brain data will only improve a text-based model if brain data contains semantic information not readily available in the corpus. | contrasting |
train_2789 | In either case, one can build a user simulation model that is the average of different user behaviors or learn a policy from a corpus that contains a variety of interaction patterns, and thus safely assume that single-agent RL techniques will work. | in the latter case if the behavior of the user changes significantly over time then the assumption that the environment is stationary will no longer hold. | contrasting |
train_2790 | Q-learning failed to converge in all cases, except for very small state space sizes. | both PHC and PHC-WoLF always converged (or in the case of 7 fruits they needed more training episodes) and performed similarly. | contrasting |
train_2791 | Also, the employment of SVM classifiers allows the incorporation of rich features for better data representation (Feng and Hirst, 2012). | hILDA's approach also has obvious weakness: the greedy algorithm may lead to poor performance due to local optima, and more importantly, the SVM classifiers are not well-suited for solving structural problems due to the difficulty of taking context into account. | contrasting |
train_2792 | 2013, we perform a sentence-level parsing for each sentence first, followed by a textlevel parsing to generate a full discourse tree for the whole document. | in addition to efficiency (to be shown in Section 6), our discourse parser has a distinct feature, which is the postediting component (to be introduced in Section 5), as outlined in dashes. | contrasting |
train_2793 | With respect to the macroaveraged F1-scores, adding the post-editing component also obtains about 1% improvement. | the overall MAFS is still at the lower end of 30% for all constituents. | contrasting |
train_2794 | Negative expressions are common in natural language text and play a critical role in information extraction. | the performances of current systems are far from satisfaction, largely due to its focus on intrasentence information and its failure to consider inter-sentence information. | contrasting |
train_2795 | The research on negation focus identification was pioneered by Blanco and Moldovan (2011), who investigated the negation phenomenon in semantic relations and proposed a supervised learning approach to identify the focus of a negation expression. | although Morante and Blanco (2012) proposed negation focus identification as one of the *SEM'2012 shared tasks, only one team (Rosenberg and Bergler, 2012) 1 participated in this task. | contrasting |
train_2796 | In (Li et al., 2005), new word detection was viewed as a binary classification problem. | these supervised models requires not only heavy engineering of linguistic features, but also expensive annotation of training data. | contrasting |
train_2797 | Comparison between LRT + LP E (or LRT + LP E + N W P ) and LRT shows that inclusion of left pattern entropy also boosts the performance apparently. | the new word probability (N W P ) has only marginal contribution to improvement. | contrasting |
train_2798 | The sentiment captured in opinionated text provides interesting and valuable information for social media services. | due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment. | contrasting |
train_2799 | Automatically extracting sentiments from usergenerated opinionated text is important in building social media services. | the complexity and diversity of the linguistic representations of sentiments make this problem challenging. | contrasting |
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