Large-scale annotation efforts typically involve several experts who may disagree with each other. We propose an approach for modeling disagreements among experts that allows providing each annotation with a confidence value (i. e. , the posterior probability that it is correct). Our approach allows computing certainty-level for individual annotations, given annotator-specific parameters estimated from data. We developed two probabilistic models for performing this analysis, compared these models using computer simulation, and tested each model' s actual performance, based on a large data set generated by human annotators specifically for this study. We show that even in the worst-case scenario, when all annotators disagree, our approach allows us to significantly increase the probability of choosing the correct annotation. Along with this publication we make publicly available a corpus of 10,000 sentences annotated according to several cardinal dimensions that we have introduced in earlier work. The 10,000 sentences were all 3-fold annotated by a group of eight experts, while a 1,000-sentence subset was further 5-fold annotated by five new experts. While the presented data represent a specialized curation task, our modeling approach is general; most data annotation studies could benefit from our methodology.
Virtually every large-scale biological project today, ranging from creation of sequence repositories, collections of three-dimensional structures, annotated experiments, controlled vocabularies and ontologies, or providing evidence from the literature in organism-specific genome databases, utilizes manual curation. A typical curation task in biology and medicine involves a group of experts assigning discrete codes to a datum, an experimental observation, or a text fragment. For example, curators of the PubMed database assign topics to each article that is registered in the database. These topics are encoded in a hierarchical MESH terminology [1] to ensure that curators have a consistent way to define an article' s content. Other curation examples include annotation of function of genes and proteins, description of genetic variation in genomes, and cataloguing human phenotypes. A standard approach to assessing quality of curation involves computation of inter-annotator agreement [2], such as a kappa-measure [3]. Manual curation is tedious, difficult, and expensive. It typically requires annotation by multiple people with variable attitudes, productivity, stamina, experience, tendency to err, and personal bias. Despite its difficulties and the imprecision in outcome, curation is critical. Existing curation approaches can be improved and enhanced with careful experimental design and appropriate modeling. This study aims to address the following questions: In this study we propose a holistic approach to quantify our certainty in individual annotations for a group of several annotators, which allows to retain the complete dataset as a basis for training and testing machine learning methods. Specifically, we suggest an internally consistent way to design annotation experiments and analyze curation data. We created two alternative probabilistic models for such analysis, tested these models with computer simulations, and then applied them to the analysis of a newly annotated corpus of roughly 10,000 sentences. Each sentence in this corpus was annotated by three experts. To test the utility of our computational predictions, we randomly sampled a subset of 1,000 sentences (out of the original 10,000) to reannotate by five new experts. Using these two rounds of annotation, we evaluated the models' predictions by comparing the three-experts-per-sentence results against the “gold standard” eight-experts-per-sentence analysis.
First, to generate the corpus, our homemade scripts extracted 10,000 full sentences randomly from diverse scientific texts, making sure that all sentences are distinct and that section-specific and topic-specific constraints are met. Specifically, we randomly selected 1,000 sentences from the PubMed database, which at the time of our analysis stored 8,039,972 article abstracts (note that not every PubMed entry comes with an abstract). We also sampled 9,000 sentences from the GeneWays corpus (368,331 full-text research articles from 100 high-impact biomedical journals). We put the following constraints on these 9,000 sentences: 2,100 sentences were sampled from articles related to WNT pathways, apoptosis, or schizophrenia research (700 sentences per topic, with random sampling within each pool of topic-specific articles). The remaining 6,900 sentences were sampled with restriction on article section: 20% of the sentences came from abstracts, 10% from introductions, 20% from methods, 25% from results, and 25% from article discussion sections. We did not process sentences in any way before the annotation. Because the current study is not concerned with automatic annotation of sentence fragments per se, we do not elaborate on machine-learning features that we described in our earlier study [4]. Second, we randomly reordered the 10,000 sentences and partitioned them into eight equal-size sets. We arranged eight annotators recruited for the first cycle of analysis into eight 3-annotator groups, assigning to each group a unique sentence set. This way each annotator analyzed three sets of sentences, and utilizing the loop-design of the analysis (see Figure 1A) we were able to computationally compare annotators' performances with each other. This concluded the first cycle of annotation. The part of this corpus on which all three annotators perfectly agreed, as well as the part on which at least two out of the three agreed, were used for training, testing and analyzing supervised machine learning methods for automatic annotation assignment, in a recent study, reported elsewhere [4]. As the models for annotation reliability introduced here are based on the above corpus, to reliably validate the models, we performed a second cycle of annotation. To do this, we recruited five additional annotators, sampled a subset of 1,000 random sentences out of the original 10,000, and asked the new annotators to annotate the 1,000-sentence subset. The result of the second cycle of annotation was a 1,000-sentence set that was annotated by five annotators per sentence in the second cycle and by three annotators per sentence in the first cycle. The whole annotated corpus is publicly available along with this manuscript (see Dataset S1). When defining guidelines for our present annotation effort [5] we aimed at distinguishing among several types of scientific statements, varying across multiple dimensions. Specifically, we tried to distinguish commonplace knowledge from original conclusions, high certainty statements from uncertain ones, experimentally supported evidence from speculations, and scientific statements from methodological or meta-statements. The goal of this effort was to generate a manually annotated corpus that can be further used to train computers to automatically perform well-defined annotation tasks at a large scale. In the long run, we hoped to learn to automatically highlight portions of research articles that fit a particular search goal. Such a goal can be, for example, to identify all original conclusions supported by experiments. Another plausible goal (out of many imaginable) is to find the scientific statements made with high certainty, with or without experimental support. A tool of this kind would be a useful addition to the armamentarium of a biomedical text-miner. We asked experts to annotate sentences along the following six dimensions (with two of them, polarity and certainty, combined), described in great detail in an earlier article [5]: In addition, each annotation of a dimension is allowed to have code Error, indicating erroneously extracted or jumbled sentence. As the focus of this work is the construction of models for annotation correctness, we next describe these models.
Despite the apparent complexity of the generative process under Model A, in its simplest form the model requires only one parameter per annotator for any number of allowed annotation values. In contrast, for Model B, given n permissible annotation values, there are n−1 independent values of γ' s and one independent value of λ (i) x|x for each annotator. As a result, for the number of fragments in a sentence that allows 9 values, Model B requires optimization of a likelihood function depending on 16 free parameters (584 for the full model), whereas the likelihood for Model A depends only on 8 (11 for the full model). It is well known that if we estimate parameters using numerical function optimization over a fixed-sized dataset, it is much easier and quicker to obtain the maximum-likelihood estimates when the number of model parameters is small. As the number of parameters increases, the data is typically insufficient to uniquely determine the parameter values, and parameter estimates may widely vary. As our experiments with simulated data illustrate (see below), the number of local optima grew exponentially with the number of permitted annotation values for Model B. While Model A also had multiple optima, their number was smaller, and only one optimum occurred within the parameter area where all annotators performed with correctness >0. 5. The multimodal shape of our likelihood functions is a direct consequence of the inability to directly observe or determine the correctness of annotation values. Multimodal likelihood surfaces are a common feature of models involving latent variables (e. g. , see [9]), suggesting that multiple explanations are possible for the same data and each corresponds to a mode on the likelihood surface. Moreover, the larger the number of parameters, the larger the number of possible configurations explaining the same dataset. One additional advantage of Model A is, when we annotate the same fragment of text along multiple dimensions, Model A can easily be altered to allow for non-independence among distinct types of annotations. (See Text S1 for details.) To test our methodology, before applying it to real annotator data, we conducted a study in which data were simulated from one of the models and then parameters were estimated under both (see Figure 3). When we obtained simulated data from Model A, the parameters estimated for Model A clustered nicely along a perfect diagonal (given that both true values and the initial optimization values of the correctness parameters were >0. 5) (yellow circles in Figure 3A). The parameters for Model B produced a much greater scatter of likelihood values, with better likelihood estimates closer to the expected values (blue circles in Figure 3A). This result (along with additional repeated-estimation analysis) indicates that poorer likelihood values for Model B correspond to convergence to the numerous local optima on the B-model likelihood surface. In one example (detailed in the Text S1), we made 300 estimates under Model B for the same simulated dataset (3 allowed annotation values). The estimation search ended in the same local optimum only 2 times out of 300; 298 sets of estimates were all distinct from each other. When simulation was performed by generating data under Model B, the parameters estimated for Model A tended to be more widely scattered than when the data was simulated under Model A (yellow circles in Figure 3B). Nevertheless, Model A estimates still tend to follow the diagonal of the plot. As expected, when estimation for Model B was initialized at the true parameter values, the resulting estimates grouped tightly around the perfect-estimate diagonal (black circles in Figure 3B). However, when estimation under Model B was initialized at a random point in the parameter space, (blue circles in Figure 3B), estimation scatter became extreme due to convergence on local optima. The simulations indicate that we can indeed obtain reasonable estimates of the annotator correctness parameters, and this was practically easier with Model A. Estimating parameters for Model B was computationally more expensive, requiring the estimation of many more parameters, while frequently settling into local optima. As such, we use Model A and the simplified B-with-thetas to analyze our real annotator data. Figure 4 shows estimates of key parameters for Model A using approximately 10,000 sentences, each annotated by three annotators. Figure 4A shows maximum likelihood estimates of the correctness parameters for the eight annotators (the first round of evaluation) and four dimensions of annotation. Surprisingly, not only did the value of correctness vary significantly among annotators, but the same annotator' s correctness fluctuated widely across the annotation tasks. The same annotator could perform extremely well at one annotation task and terribly at another (see Figures 4A and 5A for results of analysis under models A and B-with-thetas, respectively). We observed very similar absolute values of correctness and nearly identical patterns of annotator-specific correctness across dimensions under the two models. Thus, it is more likely that the features of our annotator correctness estimates reflect properties of annotator performance rather than being artifacts of model design. Estimates of conditional probabilities of agreement patterns given correctness status (denoted by α' s, see Figure 4B–E) and estimates of code frequencies (denoted by ω' s, see Figure 4F–I) also tell an interesting story. As we have noted previously [4], frequencies of annotation values for each dimension were far from uniform: The probability was almost 0. 75 that a sentence would be annotated as having a single fragment (Figure 4F). Similarly, there was a greater than 0. 60 chance that a fragment would contain either no reference to experimental evidence at all (E0) or direct evidence (E3), but not a value in between (E1 and E2, see Figure 4G); a 0. 55 chance that the sentence would be annotated as having scientific focus (Figure 4H); and a greater than 0. 75 chance that the fragment would contain the most certain positive statement (Figure 4I). Distributions of the code-frequency values, ω' s, were mirrored fairly closely by the annotation correctness distributions (γ-distributions), estimated for Model B-with-thetas, Figure 5 (B–E). The direct consequence of the skewed distribution of annotation codes is that under Model A the probability of random convergence to incorrect annotation values was high. Consider the conditional probabilities of agreement patterns given correctness states for the number of fragments in the sentence (Figure 4B). When all three annotators had incorrect annotations (III), the most likely observed agreement pattern was a perfect consensus (aaa, Figure 4B). Other dimensions of annotation showed a similar trend (Figure 4C–E). Why are these observations important? Because, depending on the annotation task, relying on annotator consensus annotations can lead to accepting erroneous annotations, while a proper stochastic modeling can rectify the problem. The online Text S1 provides all equations required to identify the annotation with the highest posterior probability for each annotated fragment of text. While there are numerous approaches for comparison of models in terms of their goodness-of-fit to data (e. g. [10]), we do not apply them in our comparison of models A and B, because comparison of the raw log-likelihood values makes application of more sophisticated approaches unnecessary. Indeed, when we apply both models to our real annotator data, the most complicated version of Model A (namely, A-with-alphas) has 11 parameters to resolve the number of sentence fragments while the simplest version of Model B has 16 parameters. The best log-likelihood values we achieved after performing hundreds of independent runs of our random-start likelihood-maximization processes with A-with-alphas and B-with-thetas were −19,215. 544 and −22,897. 744, respectively. It is clear, even without any more sophisticated model-selection approaches, that the simple Model A fits the data orders of magnitude better than the more complicated Model B. The simplified Model A (−19,289. 269), as expected, does not fit the data as well as its parameter-enriched version, but still significantly better than Model B. Curiously, estimates of annotator-specific accuracies (θ-values) are virtually identical under both versions of Model A (data not shown). That said, it is important to note that comparison of models is never absolute but is always relative to the data on which the models are being compared. In other words, despite our observations on the particular dataset, it is likely that there are datasets on which the performance of the models is reversed. The critical question regarding a study like this is whether the suggested approach is actually useful. To compare model-based predictions with external evaluations, we selected a random subset of 1,000 sentences (out of the original 10,000) and recruited five additional independent annotators to provide 5-fold re-annotation of these 1,000 sentences. The most obvious way to demonstrate the utility of our models would be to re-evaluate predictions for cases where the three original annotators provided three different annotations for the same fragment of text, and compare these annotations to those produced by the additional five independent annotators. To understand details of the underlying computation, consider the specific task of annotating the number of fragments in a sentence. For example, the original three annotators had estimated accuracies under the simplified Model A (θ-values) of 0. 91776,0. 91335, and 0. 82234, and detected 2,1, and 4 fragments in the sentence, respectively (3-way disagreement). The correctness values alone suggest that we should trust annotator 1 most and annotator 3 least. We can further quantify our trust by computing the posterior probabilities that each annotator is correct given this particular triplet of annotators and annotation values. The posterior probabilities (again, under simplified Model A) that the correct number of sentence fragments is 2,1, and 4 are 0. 4223,0. 3989, and 0. 1752, respectively. That is, we have more than twice as much confidence that annotator 1 is right than that annotator 3 is right. Furthermore, we have more than four times the confidence that the correct number of fragments is either 1 or 2 as opposed to 4. To check the validity of our prediction, we looked at five additional (independent) annotations for the same sentence: 2,2, 2,2, and 1. Combining the original three annotations with the five new ones, we obtained an 8-way annotator vote value: 2. In this case, clearly, the 8-way vote coincided with our maximum a posteriori probability (MAP) prediction. Due to the nature of our annotation protocol, where annotations are assigned to fragments rather than to complete sentences, the validity of polarity, focus, and evidence annotations was confounded by the validity of sentence segmentation (see Table 1 for an example). When annotators disagreed on the number of fragments and, especially on fragment boundaries, our analysis had to deal with small spurious fragments. To clarify, consider the case in which three annotators annotate a hypothetical sentence consisting of just three one-letter words: “A B C. ” The annotators are allowed to break the sentence into fragments and annotate fragments with one of two codes: 1 or 2. Suppose that annotator 1 broke the sentence into fragments “A” and “BC”, annotating the first fragment with code 1 and the second with code 2 – for brevity we write this as A1|BC2. Similarly, evaluator 2 produced annotation AB1|C2 – breaking the sentence also into a pair of fragments, but, unlike annotator 1, grouping A and B. The third evaluator did not break the sentence at all, assigning annotation 2 to the whole sentence: ABC2. In combining these annotations, in order to enable analysis of the results, we first find the minimal fragmentation that incorporates all breakpoints –in our case, A, B, and C. Then we re-write the original annotations by transferring codes from larger fragments to smaller ones: A1B1C2, A1B2C2, and A2B2C2, for annotators 1,2, and 3, respectively. As such, we pooled all breakpoints from the annotators to determine the final fragmentation and each of the original annotated fragments propagates its annotation down to all the final fragments composing it. We recognize that in future studies the segmentation and annotation should be performed in two stages. The first stage should focus on annotating boundaries of the fragments and finding the maximum a posteriori boundary. The second stage should involve annotation of fragment codes given the MAP sentence fragmentation. Despite this additional noise, our analysis below demonstrates that MAP predictions were significantly enriched with correct answers. The main difficulty with the practical application of Model B is that even after a large number of numerical optimization runs, starting with different initial values, we had no confidence that we had identified the global optimum of the posterior probability. Nevertheless, we used the set of parameter estimates marked with the best likelihood value and the highest prior probability observed in a set of about 100 independent runs. Our results show that even these imperfect parameter estimates provide surprisingly robust prediction results (see Table 2). For evaluating the quality of our model-specific predictions we need to establish a baseline corresponding to a naïve random-predictor method. If we consider only three-way annotation disagreements, a naïve random-predictor method would work by sampling an annotation out of three choices with a uniform probability (1/3). Similarly, the probability that two annotations out of three include the correct answer (given that one of the three answers is correct) is 2/3. Examining Table 1, we can immediately see that the number of correct MAP predictions under both models was almost invariably greater than the randomly expected number (with the one exception of the two-best-predictions analysis of Polarity—Certainty annotations). Both models appear to do their prediction job extremely well, with Model B-with-thetas performing marginally better. Despite the relatively small numbers of test cases for each type of annotation (31,157,108, and 87 three-way disagreements for distinct annotation types, see Table 2), we observe highly significant deviation from random prediction for each annotation type. The majority of our model-specific p-values, computed with Pearson' s chi-squared test, are smaller than 10−3 and a few are smaller than 10−7 and 10−10 (see Table 2), indicating the extreme improbability that our prediction success is accidental. While Model A fits the data better, Model A assumes that annotations are dependent only on the agreement pattern of judges and, given agreement pattern, are conditionally independent of their correctness. We suspect this independence assumption is violated to some extent and this explains Model B' s slight advantage in predicting the eight judge results based on the three judge data. In summary, our method picks the correct prediction (as determined by a larger panel of new additional independent experts) much more frequently than random, proving that our approach offers a practical aid to annotation tasks. We performed our probabilistic analysis using programs written in MatLab (MathWorks); all corresponding scripts are available to anyone interested. For our numerical analysis of posterior probability distributions, we used our own implementation of a simulated annealing algorithm [11], the MatLab implementation of the multidimensional simplex method, and common sense, see Dataset S2.
How can the real-world data curation efforts, such as Arabidopsis thaliana annotation [14], Mouse Genome Database [15], UniProt and Swiss-Prot [16], GenBank [17] and numerous other repositories heavily used by bench biologists, benefit from our methodology? It would be naïve on our part to expect that every curation team in the world will immediately switch to annotating each piece of data three times, using a loop design for multiple annotators (it would be nice, though). However, it is likely to benefit the curation teams to conduct small-scale annotation experiments, estimating error rates specific to the task at hand and to the group of annotator experts. Such estimates can be immediately used to assign confidence to data annotated by a single expert with a known correctness rate. Furthermore, estimates of annotator correctness are useful in conducting randomized quality control checks, where a randomly chosen datum is re-annotated by a group of three annotators with known performance metrics. We further illustrate the applicability of the method in the following example. Consider a team of curators at the Jackson Laboratory in Bar Harbor, Maine, working on curating mouse phenotypes for mouse strains with genetic differences within corresponding genomes. A genome of a given mouse strain can harbor a spectrum of variations relative to the genome of another mouse strain. Mouse phenotypes are arranged into a hierarchical terminology [15], [18] where each term is assigned to a unique code. While in some cases assignment of genetic variation to a phenotype is clear and unambiguous, in others the curators have to resolve some degree of ambiguity of assignment of rearrangement to a specific gene or genes (e. g. , when multiple genes are affected) or of genetic variation to a phenotype (e. g. , when pleiotropic variation is considered). We can directly relate such an annotation task to our modeling framework. Suppose that eight curators (1,2, …, 8) are arranged into eight groups of three experts each: (1,2, 3), (2,3, 4), (3,4, 5), … (7,8, 1). We ask curators within the same group to assign discrete phenotypic codes to the same subset of genetic variations. From the annotated data we can estimate model parameters for Models A and B as described in the paper, and estimate curator-specific error rates. Such error-rates are immediately useful in order to: The above example illustrates the applicability and potential utility of the models within the setting of a current and ongoing curation effort.
Data annotation (manual data curation) tasks are at the very heart of modern biology. Experts performing curation obviously differ in their efficiency, attitude, and precision, but directly measuring their performance is not easy. We propose an experimental design schema and associated mathematical models with which to estimate annotator-specific correctness in large multi-annotator efforts. With these, we can compute confidence in every annotation, facilitating the effective use of all annotated data, even when annotations are conflicting. Our approach retains all annotations with computed confidence values, and provides more comprehensive training data for machine learning algorithms than approaches where only perfect-agreement annotations are used. We provide results of independent testing that demonstrate that our methodology works. We believe these models can be applied to and improve upon a wide variety of annotation tasks that involve multiple annotators.
computational biology/literature analysis
computational biology/bio-ontology
computational biology/genomics
How to Get the Most out of Your Curation Effort