acl acl2013 acl2013-265 knowledge-graph by maker-knowledge-mining

265 acl-2013-Outsourcing FrameNet to the Crowd


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Author: Marco Fossati ; Claudio Giuliano ; Sara Tonelli

Abstract: We present the first attempt to perform full FrameNet annotation with crowdsourcing techniques. We compare two approaches: the first one is the standard annotation methodology of lexical units and frame elements in two steps, while the second is a novel approach aimed at acquiring frames in a bottom-up fashion, starting from frame element annotation. We show that our methodology, relying on a single annotation step and on simplified role definitions, outperforms the standard one both in terms of accuracy and time.

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 eu , l Abstract We present the first attempt to perform full FrameNet annotation with crowdsourcing techniques. [sent-2, score-0.217]

2 We compare two approaches: the first one is the standard annotation methodology of lexical units and frame elements in two steps, while the second is a novel approach aimed at acquiring frames in a bottom-up fashion, starting from frame element annotation. [sent-3, score-1.353]

3 We show that our methodology, relying on a single annotation step and on simplified role definitions, outperforms the standard one both in terms of accuracy and time. [sent-4, score-0.219]

4 Existing frame annotation tools, such as Salto (Burchardt et al. [sent-6, score-0.583]

5 , 2002) foresee this two-step approach, in which annotators first select a frame from a large repository of possible frames (1,162 frames are currently listed in the online version of the resource), and then assign the FE labels constrained by the chosen frame to LU dependents. [sent-8, score-1.301]

6 In this paper, we argue that such workflow shows some redundancy which can be addressed by radically changing the annotation methodology and performing it in one single step. [sent-9, score-0.256]

7 Our novel annotation approach is also more compliant with the definition of frames proposed in Fillmore (1976): in his seminal work, Fillmore postulated that the meanings of words can be understood on the basis of a semantic frame, i. [sent-10, score-0.281]

8 a description of a type of event or entity and the participants in it. [sent-12, score-0.043]

9 This implies that frames can be distinguished one from another on the basis of the participants involved, thus it seems more cognitively plausible to start from the FE annotation to identify the frame expressed in a sentence, and not the contrary. [sent-13, score-0.768]

10 The goal of our methodology is to provide full frame annotation in a single step and in a bottomup fashion. [sent-14, score-0.646]

11 Instead of choosing the frame first, we focus on FEs and let the frame emerge based on the chosen FEs. [sent-15, score-0.995]

12 We believe this approach complies better with the cognitive activity performed by annotators, while the 2-step methodology is more artificial and introduces some redundancy because part of the annotators’ choices are replicated in the two steps (i. [sent-16, score-0.16]

13 in order to assign a frame, annotators implicitly identify the participants also in the first step, even if they are annotated later). [sent-18, score-0.133]

14 Another issue we investigate in this work is how semantic roles should be annotated in a crowdsourcing framework. [sent-19, score-0.168]

15 This task is particularly complex, therefore it is usually performed by expert annotators under the supervision of linguis- tic experts and lexicographers, as in the case of FrameNet. [sent-20, score-0.163]

16 In NLP, different annotation efforts for encoding semantic roles have been carried out, each applying its own methodology and annotation guidelines (see for instance Ruppenhofer et al. [sent-21, score-0.341]

17 In this work, we present a pilot study in which we assess to what extent role descriptions meant for ‘linguistics experts’ are also suitable for annotators from the crowd. [sent-24, score-0.12]

18 Moreover, we show how a simplified version ofthese descriptions, less bounded to a specific linguistic theory, improve the annotation quality. [sent-25, score-0.188]

19 2 Related work The construction of annotation datasets for NLP tasks via non-expert contributors has been ap742 Proce dingSsof oifa, th Beu 5l1gsarti Aan,An u aglu Mste 4e-ti9n2g 0 o1f3 t. [sent-26, score-0.147]

20 , 2009) was meant to gather a corpus with coreference resolution annotations. [sent-33, score-0.057]

21 (2008) described design and evaluation guidelines for five natural language micro-tasks. [sent-35, score-0.036]

22 However, they explicitly chose a set of tasks that could be easily understood by nonexpert contributors, thus leaving the recruitment and training issues open. [sent-36, score-0.033]

23 The semantic role labeling problem has been recently addressed via crowdsourcing by Hong and Baker (201 1). [sent-39, score-0.137]

24 Furthermore, Baker (2012) highlighted the crucial role of recruiting people from the crowd in order to bypass the need for linguistics expert annotations. [sent-40, score-0.115]

25 Nevertheless, Hong and Baker (201 1) focused on the frame discrimination task, namely selecting the correct frame evoked by a given lemma. [sent-41, score-1.22]

26 Such task is comparable to the word sense disambiguation one as per (Snow et al. [sent-42, score-0.058]

27 3 Experiments In this section, we describe the anatomy and discuss the results of the tasks we outsourced to the crowd via the CrowdFlower1 platform. [sent-44, score-0.068]

28 Cheating risk is minimized by adding gold units, namely data for which the requester already knows the answer. [sent-46, score-0.174]

29 If a worker misses too many gold answers within a given threshold, he or she will be flagged as untrusted and his or her judgments will be automatically discarded. [sent-47, score-0.447]

30 Worker switching effect Depending on their accuracy in providing answers to gold units, workers may switch from a trusted to an untrusted status and vice versa. [sent-48, score-0.647]

31 In practice, a worker submits his or her responses via a web page. [sent-49, score-0.164]

32 Each page contains one gold unit and a variable number of regular units that can be set by the requester dur- ing the calibration phase. [sent-50, score-0.284]

33 If a worker moves back to the trusted status, his or her previous contribution is added to the results as free extra judgments. [sent-53, score-0.246]

34 Such phenomenon typically occurs when the complexity of gold units is high enough to induce low agreement in workers’ answers. [sent-54, score-0.105]

35 Thus, the requester is constrained to review gold units and to eventually forgive workers who missed them. [sent-55, score-0.45]

36 This has massively happened in our experiments and is one of the main causes of the overall cost decrease and time increase. [sent-56, score-0.047]

37 Cost calibration The total cost of a generic crowdsourcing task is naturally bound to a data unit. [sent-57, score-0.206]

38 This represents an issue in most of our experiments, as the number of questions per unit (i. [sent-58, score-0.123]

39 a sentence) varies according to the number of frames and FEs evoked by the LU contained in a sentence. [sent-60, score-0.232]

40 In order to enable cost comparison, for each experiment we need to use the average num- ber of questions per sentence as a multiplier to a constant cost per sentence. [sent-61, score-0.304]

41 We set the payment per working page to 5 $ cents and the number of sentences per page to 3, resulting in 1. [sent-62, score-0.206]

42 1 Assessing task reproducibility and worker behavior change Since our overall goal is to compare the performance of FrameNet annotation using our novel workflow to the performance of the standard, 2step approach, we first take into account past related works and try to reproduce them. [sent-65, score-0.303]

43 To our knowledge, the only attempt to annotate frame information through crowdsourcing is the one presented in Hong and Baker (201 1), which however did not include FE annotation. [sent-66, score-0.588]

44 (a) Workers are invited to read a sentence where a LU is bolded. [sent-68, score-0.071]

45 is combined with the set of frames evoked by the given LU, as well as the None choice. [sent-70, score-0.232]

46 Finally, (c) workers must select the correct frame. [sent-71, score-0.273]

47 A set of example sentences corresponding to each possible frame is provided in the instructions to facilitate workers. [sent-72, score-0.477]

48 As a preliminary study, we wanted to assess to what extent the proposed task could be reproduced and if workers reacted in a comparable way over time. [sent-73, score-0.369]

49 Hong and Baker (201 1) did not publish the input datasets, thus we ignore which sen743 Table 1: Comparison of the reproduced frame discrimination task as per (Hong and Baker, 2011) tences were used. [sent-74, score-0.72]

50 Besides, the authors computed accuracy values directly from the results upon a majority vote ground truth. [sent-75, score-0.031]

51 5 expertannotated sentences as gold-standard data for immediate accuracy computation. [sent-85, score-0.031]

52 For the latter, we only show accuracy values, as the number of sentences was set to a constant value of 18, 2 of which were gold. [sent-87, score-0.031]

53 If we assume that the crowd-based ground truth in 2011 experiments is approximately equivalent to the expert one, workers seem to have reacted in a similar manner compared to Hong and Baker’s values, except for rip. [sent-88, score-0.408]

54 2 General task setting We randomly chose the following LUs among the set of all verbal LUs in FrameNet evoking 2 frames each: disappear. [sent-91, score-0.168]

55 We considered verbal LUs as they usually have more overt arguments in a sentence, so that we were sure to provide workers with enough candidate FEs to annotate. [sent-96, score-0.273]

56 Linguistic tasks in crowdsourcing frameworks are usually decomposed to make them accessible to the crowd. [sent-97, score-0.111]

57 Hence, we set the polysemy of LUs to 2 to ensure that all experiments are executed using the smallest-scale subtask. [sent-98, score-0.031]

58 More frames can then be handled by just replicating the experiments. [sent-99, score-0.142]

59 3 2-step approach After observing that we were able to achieve similar results on the frame discrimination task as in previous work, we focused on the comparison between the 2-step and the 1-step frame annotation approaches. [sent-101, score-1.207]

60 We first set up experiments that emulate the former approach both in frame discrimination and FEs annotation. [sent-102, score-0.624]

61 Given the pipeline nature of the approach, errors in the frame discrimination step will affect FE recognition, thus impacting on the final accuracy. [sent-104, score-0.624]

62 The magnitude of such effect strictly depends on the number of FEs associated with the wrongly detected frame. [sent-105, score-0.033]

63 1 Frame discrimination Frame discrimination is the first phase of the 2step annotation procedure. [sent-108, score-0.4]

64 The task is modeled as per Sec- Discussion Table 2 gives an insight into the results, which confirm the overall good accuracy as per the experiments discussed in Section 3. [sent-112, score-0.147]

65 2 Frame elements recognition We consider all sentences annotated in the previous subtask with the frame assigned by the workers, even if it is not correct. [sent-116, score-0.504]

66 (a) Workers are invited to read a sentence where a LU is bolded and the frame that was identified in the first step is provided as a title. [sent-118, score-0.574]

67 (b) A list of FE definitions is then shown together with the FEs text chunks. [sent-119, score-0.111]

68 Finally, (c) workers must match each definition with the proper FE. [sent-120, score-0.273]

69 FD stands for Frame Discrimination, FER for FEs Recognition Original: The conscious entity, generally a person, that performs the intentional action that results in the damage to the Patient. [sent-124, score-0.164]

70 Manually simplified: This element describes the person that performs the intentional action resulting in the damage to another person or object. [sent-125, score-0.263]

71 Automatic system: What that performs the intentional action that results in the damage to the Patient? [sent-126, score-0.164]

72 complex definitions as Avoid variability in FE definitions, try to Amvaokeid th veamria homogeneous (e. [sent-131, score-0.111]

73 they s throyul tod all start with “This element describes. [sent-133, score-0.029]

74 Although these changes (especially the last item) may make FE definitions less precise from one achieved a better accuracy and a lower num- ber of untrusted annotators compared to the others. [sent-138, score-0.381]

75 Therefore, we use the simplified definitions in both the 2-step and the 1-step approach (Section 3. [sent-139, score-0.193]

76 The total number of answers differs from the total number of trusted judgments, since the average value of questions per sentence amounts to 1. [sent-142, score-0.244]

77 2 First of all, we notice an increase in the number of untrusted judgments. [sent-144, score-0.146]

78 This is caused by a generally low inter-worker agreement on gold sentences due to FE definitions, which still present a certain degree of complexity, even after simplification. [sent-145, score-0.046]

79 We inspected the full reports sentence by sentence and observed a propagation of incorrect judgments when a sentence involves an unclear FE definition. [sent-146, score-0.129]

80 As FE definitions may mutually include mentions of other FEs from the same frame, we believe this circularity generated confusion. [sent-147, score-0.111]

81 4 1-step approach Having set the LU polysemy to 2, in our case a sentence S always contains a LU with 2 possible frames (f1, f2), but only conveys one, e. [sent-149, score-0.199]

82 Furthermore, we allow workers to select the None answer. [sent-157, score-0.273]

83 In practice, we ask a total amount of |E1 ∪ E2 | + 2 questions per sentence aSm. [sent-158, score-0.122]

84 oIunn tth oisf way, we le|t + +th 2e qfuraemsteio directly emerge from the FEs. [sent-159, score-0.041]

85 If workers correctly answer None to a FE definition d ∈ E2, the probability that S etovo ake FEs f1ifnicnrietioasnes d. [sent-160, score-0.273]

86 Modeling Figure 1 displays a screenshot of the worker interface. [sent-161, score-0.191]

87 The sentence Karen threw he r arms round my ne ck spi l ing l , taieoln etxbei cn otgiatmriaep shmaircoerpeno itn ntueiotcifev s iea wrnildy(hfpaoerds iaon sptsoa)ns,ictaeinv, esoietman- tcHh orwoawme. [sent-166, score-0.052]

88 atWanidlsetahsekCtoAaUnS EotMatOeTbIoOtNh 745 Figure 1: 1-step approach worker interface core FEs, respectively as regular and cross-frame units. [sent-172, score-0.164]

89 Instead of precision and recall, we are thus able to directly compute workers’ accuracy upon a majority vote. [sent-174, score-0.031]

90 We envision an improvement with respect to the 2step methodology, as we avoid the proven risk of error propagation originating from wrongly annotated frames in the first step. [sent-175, score-0.255]

91 This demon- strates the hypothesis we stated in Section 1 on the cognitive plausibility of a bottom-up approach for frame annotation. [sent-178, score-0.477]

92 Nevertheless, the cost is sensibly higher due to the higher number of questions that need to be addressed, in average 4. [sent-180, score-0.085]

93 Untrusted judgments seriously grow, mainly because of the cross-frame gold complexity. [sent-183, score-0.097]

94 Workers seem puzzled by the presence of None, which is a required answer for such units. [sent-184, score-0.056]

95 If we consider the English FrameNet annotation agreement values between experts reported by Pad o´ and Lapata (2009) as the upper bound (i. [sent-185, score-0.159]

96 4 Conclusion In this work, we presented an approach to perform frame annotation with crowdsourcing techniques, based on a single annotation step and on manu- ally simplified FE definitions. [sent-190, score-0.882]

97 Since the results seem promising, we are currently running larger scale experiments with the full set ofFrameNet 1. [sent-191, score-0.03]

98 Future work will include the investigation of a frame assignment strategy. [sent-195, score-0.477]

99 Hence, we need a confidence score to determine which frame emerges if workers selected contradictory answers in a subset of cross-frame FE definitions. [sent-197, score-0.79]

100 Divide and conquer: crowdsourcing the creation of cross-lingual textual entailment corpora. [sent-245, score-0.111]


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