Error Analysis. IAR makes an analysis of the reported problem, tries to reproduce the problem where applicable and feasible, and isolates the Error, if any. Support does not include an analysis of the Licensee’s applications or in normal cases interoperability between the Product and other products or software. The Licensee’s obligation in this respect is to provide, to a reasonable extent, information about the suspected Error based on the instructions from IAR, in a timely manner and coherent form.
Error Analysis. We also used the SCLITE (score speech recogni- tion system output) program from the NIST scor- Freq Reference ==> Hypothesis 16 သူ မ ==> သူ 14 ခင်ဗျား ==> မင်း 9 ပါတယ် ==> တယ် 8 ပါ→ူ း ==> →ူ း 5 →ာေတွ ==> →ာ 5 မင်းကု ိ ==> ကု ိ 5 မလား ==> မှ ာလား 5 လား ==> သလား 5 အ့ ဲဒါကု ိ ==> ကု ိ 4 ခ့ ဲ→ူ း ==> →ူ း 4 →ူ းလား ==> ရှ ိလား 4 မင်းရဲ ့ ==> မင်း 4 လဲ ==> သလဲ 4 သူ ့ ==> သူ မ ### Paraphrasing Error ### SOURCE:ငှ ား ဟှ ားဟိ အီ ေလ ။ Table 3: The top 15 confusion pairs of OSM model for Dawei-Myanmar machine translation with word segmentation
Error Analysis. The process of crafting numerical results from the given financial problem is quite long. Given the starting point of the problem at hand we must convert this into a mathematical model. In this process modelling error will arise. Next the mathemat- ical model must be numerically approximated. In this step of forming an algebraic representation, discretization errors are introduced. Finally, this numerical approx- imation needs to be solved in some way. The step from approximation to results is affected by rounding errors. See 1.1. With this in mind error analysis is a key component of any numerical method. In this paper we will focus on discetization error. In considering discretization error there are two main components. Space discretization represented by h. In this problem it is actually price of the underlying
Error Analysis. The FEM1D output would then be compared to an analytical solution solved ex- plicitly in a MATLAB subroutine on the nodes that FEM1D solved on. The .m file would produce a matrix with the same dimensions as the FEM1D matrix. In this example, the size of V was 46x2021. The subroutine can be seen in Appendix A.1. It solves for w as well as the Put and Call, given inputs of a risk free rate, volatility, and the size dimensions of the matrix. It does so by running through equation (2.23) in MATLAB notation. By running the subroutine and graphing this example we see 4.5 Figure 4.5: Analytic Solution This is quite similar to the solution solved by FEM1D. To get a better sense of how close the two we can measure the difference of the two. Doing so we see 4.6 We must analyze this picture with an eye towards the expected elements of error. We see that for a portion of the mesh there is nearly zero visible error. This is clearly good and validates the inputs and the output of FEM1D. However, two major visible sources of error arise. One relates to the boundary that is closest to time zero and nearing the S boundary. This error was expected, as we have truncated the infinite domain to a finite boundary point. The reason the error increases as time approaches zero is because the equation used a final condition. That is, we have an exact solution Figure 4.6: Difference of Analytic and Numeric Solutions at time 10, so there should be no error there. The strength of this initial condition is keeping error down in the area near time 10. However, as it gets away from the certainty of this final condition while also moving towards the truncated boundary point, error arises. The other anomaly visible in the graph is the set of spikes that arises at S = 35, as time approaches the end boundary. This is actually not intuitive at all but is an explained occurrence in numerical analysis. It relates to the use of .5 for theta [4]. While the visualization is a strong tool for comparison it fails to emphasize the errors relating to ∆t and h . It was important to find the best way to encapsulate the simulation’s error in a concise numerical fashion. The most appropriate way for measuring the error for a parabolic problem is either
Error Analysis. An error analysis is manually performed on 100 resumes. Errors mainly result from the following fields:
Error Analysis. In the xxxx of SLA research and analysis of learner errors, the preferred method was based in Xxxxxx'x (1967) Error Analysis. As in the present study, many SLA researchers still use Errors Analysis in order to study learner language. Error Analysis describes errors in learner language but is not always viewed as a sufficient analytical tool in itself. It is often combined with contrastive analysis, pragmatics, or discourse analysis (Köhlmyr, 2001). The theories behind Error Analysis are based on the belief that language acquisition is a mentalist process and that the errors made by a learner gives an insight as to what is already acquired and what is not. Previously, the errors made by learners were considered a problem that needed to be eliminated and they were merely viewed as the product of flawed learning or were attributed to the interference of the learner's native language. With EA, the errors "are to be viewed as indications of a learner’s attempt to figure out some system, that is, to impose regularity on the language the learner is exposed to. As such, they are evidence of an underlying rule-governed system" (Xxxx & Selinker, 2008, p. 102). When using Error Analysis for the present study, the identification of the errors was one of the more difficult tasks at hand. In order to properly define an error, there are a few delimitations that are necessary. First of all, it is necessary to define what an error actually is. In this essay, the definition of an error is that of Xxxxxx (1967) who differentiates between an error and a mistake as follows; a mistake is purely a random inaccuracy in performance whereas an error is proof of a lack of linguistic competence (Xxxxxx, 1967). In many cases, this distinction is impossible to make since a single lapse in performance, e.g. one occurrence of incorrect spelling, could be interpreted as a spelling mistake or a grammatical error, if the incorrect spelling happened to occur with a verb ending and the researcher is looking for errors regarding tense. In the present study, no distinction has been made between errors and mistakes, unless it is obvious that the inaccuracy is the result of a slip of the pen or the handwriting makes it impossible to discern what is intended. Therefore, all grammatically incorrect sentences regarding subject-verb agreement have been included in this study. However, all identified errors are not included, only the ones specifically concerning subject-verb agreement. Furthermore, th...
Error Analysis. After analyzing 100 resumes where the predicted labels are not correct, we found that 46 of them are due to overestimation (e.g., a resume rated as NQ is labeled as CRCI) and 54 of them are because of underestimation (e.g., a resume rated as CRCI is labeled as NQ). The detailed statistics are shown in Table 5.5, where 40.74% of CRC II resumes are underestimated as CRC I and 52.17% of NQ resumes are overestimated as CRC I. In addition, compared the results with the annotation guidelines, we can see the adjacent positions are difficult to be distinguished. For example, the majority of requirements for the adjacent CRC positions, CRC I and CRC II are quite similar, but they have different requirements for the number of years on research experience. U: True - Predicted No. O: True - Predicted No. CRC I - NQ 13 NQ - CRC I 24 CRC II - CRC I 22 CRC I - CRC II 3 CRC III - CRC II 1 CRC II - CRC III 11 CRC IV - CRC III 4 CRC I - CRC III 8 Table 5.5: Error analysis on TST. U: Underestimated resumes. O: Overesti- mated resumes.
Error Analysis. From the above question type analysis we know that the main error can be found in three types of questions which are who, how and why questions, so we extract 100 specific error examples of those three question types to analyze the specific Type Dist. EM SM UM Where 18.16 13.57 66.1(±0.5) 79.9(±0.7) 89.8(±0.7) When What Who How Why 18.48 18.82
Error Analysis. Since Hedonometer fails to detect any events for both the unfiltered dataset and the dataset preprocessed with location specification, an extensive error analysis is performed on explaining such inefficiency of Hedonometer. As shown below, Hedonometer tends to mark most (about 90% of all) tweets as neutral, and for tweets that are not categorized into neutral, they are more likely to be marked as positive than negative, whereas Stanford CoreNLP shows the proportion of negative tweets largely exceeds that of positive tweets. Date Positive Neutral Negative March 1 8.2% 89.6% 2.2% March 2 8.9% 89.3% 1.8% March 3 8.4% 89.4% 2.2% March 4 8.7% 89.9% 1.4% March 5 8.4% 90.0% 1.6% March 6 7.9% 90.4% 1.7% March 7 8.1% 89.7% 2.3% March 8 8.3% 89.6% 2.1% March 9 8.0% 90.3% 1.7% March 10 8.7% 89.4% 1.9% March 11 8.8% 88.8% 2.4% March 12 8.3% 89.6% 2.1% Table 5.7: Percentage of positive/neutral/negative New-York-related tweets on each day calculated by Hedonometer Misclassification After manually examining the tweets that have been categorized into “neutral” by Hedonometer, the researcher notices that He- donometer sometimes classifies tweets as neutral even if the sentiment is distinctly negative. As shown in the table below, the three examples convey negative emotions but all are marked as neutral tweet by Hedonometer. Errors in this category have no apparent cause to understand why Hedonometer makes such assessment. Because of the large amount of tweets, deciding what proportion is misclassified requires too much human labor for close-up evaluation. Table 5.8: Examples of neutral tweets marked by Hedonometer A possible explanation for such misclassification is that Hedonometer has an inefficient parser. For instance, in the second sentence from the table above, the word “can’t” is parsed as “ca” in the word list, which wipes off the negative meaning carried by the original word. Nonetheless, even though sentence 1 has most of the words correctly dissected, the sentiment value is still imprecise. Given this analysis, we hope the challenges caused by Hedonometer are well demonstrated and become easier to be overcome in future studies. Researchers can consider utilize sentiment analysis tools that also employ a ternary classification method but have a higher accuracy when applied to social media content than Hedonometer.
