acl acl2011 acl2011-85 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Altaf Rahman ; Vincent Ng
Abstract: While world knowledge has been shown to improve learning-based coreference resolvers, the improvements were typically obtained by incorporating world knowledge into a fairly weak baseline resolver. Hence, it is not clear whether these benefits can carry over to a stronger baseline. Moreover, since there has been no attempt to apply different sources of world knowledge in combination to coreference resolution, it is not clear whether they offer complementary benefits to a resolver. We systematically compare commonly-used and under-investigated sources of world knowledge for coreference resolution by applying them to two learning-based coreference models and evaluating them on documents annotated with two different annotation schemes.
Reference: text
sentIndex sentText sentNum sentScore
1 edu a Abstract While world knowledge has been shown to improve learning-based coreference resolvers, the improvements were typically obtained by incorporating world knowledge into a fairly weak baseline resolver. [sent-3, score-0.678]
2 Moreover, since there has been no attempt to apply different sources of world knowledge in combination to coreference resolution, it is not clear whether they offer complementary benefits to a resolver. [sent-5, score-0.577]
3 We systematically compare commonly-used and under-investigated sources of world knowledge for coreference resolution by applying them to two learning-based coreference models and evaluating them on documents annotated with two different annotation schemes. [sent-6, score-0.928]
4 1 Introduction Noun phrase (NP) coreference resolution is the task of determining which NPs in a text or dialogue refer to the same real-world entity. [sent-7, score-0.367]
5 The difficulty of the task stems in part from its reliance on world knowledge (Charniak, 1972). [sent-8, score-0.188]
6 Having the (world) knowledge that Martha Stewart is a celebrity would be helpful for establishing the coreference relation between the two NPs. [sent-14, score-0.408]
7 However, since these heuristics are not perfect, complementing them with world knowledge would be an important step towards bringing coreference systems to the next level of performance. [sent-16, score-0.471]
8 Despite the usefulness of world knowledge for coreference resolution, early learning-based coref- erence resolvers have relied mostly on morphosyntactic features (e. [sent-17, score-0.545]
9 With recent advances in lexical semantics research and the development of large-scale knowledge bases, researchers have begun to employ world knowledge for coreference resolution. [sent-22, score-0.575]
10 World knowledge is extracted primarily from three data sources, web-based encyclopedia (e. [sent-23, score-0.083]
11 While each of these three sources of world knowledge has been shown to improve coreference resolution, the improvements were typically obtained by incorporating world knowledge (as features) into a baseline resolver composed of a rather weak coreference model (i. [sent-31, score-1.047]
12 First, can world knowledge still offer benefits when used in combination with a richer set of features? [sent-38, score-0.231]
13 Second, since automatically extracted world knowledge is typically noisy (Ponzetto and Poesio, 2009), are recently-developed coreference models more noisetolerant than the mention-pair model, and if so, can they profit more from the noisily extracted world knowledge? [sent-39, score-0.658]
14 Finally, while different world knowlProceedinPgosrt olafn thde, 4 O9rtehg Aonn,n Juuanle M 1e9e-2tin4g, 2 o0f1 t1h. [sent-40, score-0.126]
15 We seek answers to these questions by conducting a systematic evaluation ofdifferent world knowledge sources for learning-based coreference resolution. [sent-43, score-0.509]
16 Specifically, we (1) derive world knowledge from encyclopedic sources that are underinvestigated for coreference resolution, including FrameNet (Baker et al. [sent-44, score-0.509]
17 Our evaluation corpus contains 410 documents, which are coreference-annotated using the ACE annotation scheme as well as the OntoNotes annotation scheme (Hovy et al. [sent-48, score-0.122]
18 By evaluating on two sets of coreference annotations for the same set of documents, we can determine whether the usefulness of world knowledge sources for coreference resolution is dependent on the underlying annotation scheme used to annotate the documents. [sent-50, score-0.962]
19 2 Preliminaries In this section, we describe the corpus, the NP extraction methods, the coreference models, and the evaluation measures we will use in our evaluation. [sent-51, score-0.283]
20 1 Data Set We evaluate on documents that are coreferenceannotated using both the ACE annotation scheme and the OntoNotes annotation scheme, so that we can examine whether the usefulness of our world knowledge sources is dependent on the underlying coreference annotation scheme. [sent-53, score-0.705]
21 ACE and OntoNotes employ different guidelines to annotate coreference chains. [sent-56, score-0.325]
22 A major 815 difference between the two annotation schemes is that ACE only concerns establishing coreference chains among NPs that belong to the ACE entity types, whereas OntoNotes does not have this restriction. [sent-57, score-0.333]
23 Hence, the OntoNotes annotation scheme should produce more coreference chains (i. [sent-58, score-0.365]
24 , nonsingleton coreference clusters) than the ACE annotation scheme for a given set of documents. [sent-60, score-0.344]
25 Another difference between the two annotation schemes is that singleton clusters are annotated in ACE but not OntoNotes. [sent-62, score-0.117]
26 As discussed below, the presence of singleton clusters may have an impact on NP extraction and coreference evaluation. [sent-63, score-0.371]
27 2 NP Extraction Following common practice, we employ different methods to extract NPs from the documents annotated with the two annotation schemes. [sent-65, score-0.118]
28 In other words, doing so could substantially simplify the coreference task. [sent-72, score-0.283]
29 Consequently, we follow the approach adopted by traditional learning-based resolvers and employ an NP chunker to extract NPs. [sent-73, score-0.101]
30 3 Coreference Models We evaluate the utility of world knowledge using the mention-pair model and the cluster-ranking model. [sent-79, score-0.188]
31 These features can also be categorized based on whether they are relational or not. [sent-85, score-0.09]
32 Relational features capture the relationship between NPj and NPk, whereas nonrelational features capture the linguistic property of one of these two NPs. [sent-86, score-0.107]
33 ’s (2001) method for creating training instances: we create (1) a positive instance for each anaphoric NP, NPk, and its closest antecedent, NPj ; and (2) a negative instance for NPk paired with each of the intervening NPs, NPj+1, NPj+2, . [sent-89, score-0.161]
34 The classification of a training instance is either positive or negative, depending on whether the two NPs are coreferent in the associated text. [sent-93, score-0.101]
35 Specifically, each NP, NPk, is compared in turn to each preceding NP, NPj, from right to left, and NPj is selected as its antecedent if the pair is classified as coreferent. [sent-96, score-0.143]
36 The process terminates as soon as an antecedent is found for NPk or the beginning of the text is reached. [sent-97, score-0.115]
37 First, since each candidate antecedent for an NP to be resolved (henceforth an active NP) is considered independently of the others, this model only determines how good a candidate antecedent is relative to the active NP, but not how good a candidate antecedent is relative to other candidates. [sent-99, score-0.278]
38 So, it fails to answer the critical question of which candidate antecedent is most probable. [sent-100, score-0.098]
39 Second, it has limitations in its expressiveness: the information extracted from the two NPs alone may not be sufficient for making a coreference decision. [sent-101, score-0.304]
40 Specifically, the CR model ranks the preceding clusters for an active NP so that the highest-ranked cluster is the one to which the active NP should be linked. [sent-112, score-0.22]
41 Employing a ranker addresses the first weakness, as a ranker allows all candidates to be compared simultaneously. [sent-113, score-0.124]
42 Considering preceding clusters rather than antecedents as candidates addresses the second weakness, as cluster-level features (i. [sent-114, score-0.158]
43 , features that are defined over any subset of NPs in a preceding cluster) can be employed. [sent-116, score-0.104]
44 Since the CR model ranks preceding clusters, a training instance i(cj, NPk) represents a preceding cluster, cj, and an anaphoric NP, NPk. [sent-118, score-0.203]
45 Each instance consists of features that are computed based solely on NPk as well as cluster-level features, which describe the relationship between cj and NPk. [sent-119, score-0.157]
46 (2007), we create cluster-level features from the relational features in our feature set using four predicates: NONE, MOSTFALSE, MOST-TRUE, and ALL. [sent-121, score-0.178]
47 Specifically, for each relational feature X, we first convert X into an equivalent set of binary-valued features if it is multivalued. [sent-122, score-0.089]
48 We train a cluster ranker to jointly learn anaphoricity determination and coreference resolution using SVMlight’s ranker-learning algorithm. [sent-124, score-0.486]
49 Specifically, for each NP, NPk, we create a training instance between NPk and each preceding cluster cj using the features described above. [sent-125, score-0.329]
50 Since we are learning a joint model, we need to provide the ranker with the option to start a new cluster by creating an additional training instance that contains the nonrelational features describing NPk. [sent-126, score-0.225]
51 The rank value of a training instance i(cj, NPk) created for NPk is the rank of cj among the competing clusters. [sent-127, score-0.162]
52 If NPk is non-anaphoric, its rank is LOW unless it is the additional training instance described above, which has rank HIGH. [sent-129, score-0.082]
53 After training, the cluster ranker processes the NPs in a test text in a left-to-right manner. [sent-130, score-0.119]
54 For each active NP, NPk, we create test instances for it by pairing it with each of its preceding clusters. [sent-131, score-0.163]
55 To allow for the possibility that NPk is non-anaphoric, we create an additional test instance as during training. [sent-132, score-0.088]
56 If the additional test instance is assigned the highest rank value, then we create a new cluster containing NPk. [sent-134, score-0.167]
57 Note that the partial clusters preceding NPk are formed incrementally based on the predictions of the ranker for the first k − 1 NPs. [sent-136, score-0.181]
58 POn| ttohe o bottaheinr hand, CEAF finds the best one-to-one alignment between the key clusters and the response clusters. [sent-145, score-0.098]
59 A complication arises when B3 is used to score a response partition containing automatically extracted NPs. [sent-146, score-0.093]
60 Hence, if the response is generated using goldstandard NPs, then every NP in the response is mapped to some NP in the key and vice versa. [sent-148, score-0.088]
61 To address this problem, we set the recall and precision of a twinless NP to zero, regardless of whether the NP appears in the key or the response. [sent-154, score-0.082]
62 Additionally, in order not to over-penalize a response partition, we remove all the twinless NPs in the response that are singletons. [sent-156, score-0.145]
63 Since B3 and CEAF align NPs/clusters, the lack of singleton clusters in the OntoNotes annotations implies that the resulting scores reflect solely how well a resolver identifies coreference links and do not take into account how well it identifies singleton clusters. [sent-158, score-0.453]
64 3 Extracting World Knowledge In this section, we describe how we extract world knowledge for coreference resolution from three different sources: large-scale knowledge bases, coreference-annotated data and unannotated data. [sent-159, score-0.676]
65 1 World Knowledge from Knowledge Bases We extract world knowledge from two large-scale knowledge bases, YAGO and FrameNet. [sent-161, score-0.274]
66 1 Extracting Knowledge from YAGO We choose to employ YAGO rather than the more popularly-used Wikipedia due to its potentially richer knowledge, which comprises 5 million facts extracted from Wikipedia and WordNet. [sent-164, score-0.087]
67 , 2011), we employ the two relation types that we believe are most useful for coreference resolution, TYPE and MEANS. [sent-168, score-0.325]
68 For instance, the two triples (E inst e in, MEANS, Albe rtE inst e in) and (E inst e in, MEANS, Al fredE inst e in) denote the facts that Einstein may refer to the physicist Albert Einstein and the musicologist Alfred Einstein, respectively. [sent-172, score-0.312]
69 We incorporate the world knowledge from YAGO into our coreference models as a binary-valued feature. [sent-180, score-0.471]
70 On the other hand, if the CR model is used, the YAGO feature for an instance involving NPk and preceding cluster c will have the value 1 if and only if NPk has a TYPE or MEANS relation with any of the NPs in c. [sent-182, score-0.184]
71 Since knowledge extraction from webbased encyclopedia is typically noisy (Ponzetto and Poesio, 2009), we use YAGO to determine whether two NPs have a relation only if one NP is a named entity (NE) of type person, organization, or location according to the Stanford NE recognizer (Finkel et al. [sent-183, score-0.106]
72 As a schematic representation of a situation, a frame contains the lexical predicates that can invoke it as well as the frame elements (i. [sent-189, score-0.201]
73 This frame has COMMUNICATOR and EVALUEE as its core frame elements and ADDRESSEE as its noncore frame elements, and can be invoked by more than 40 predicates, such as acclaim, accuse, com818 mend, decry, denounce, praise, and slam. [sent-193, score-0.21]
74 To better understand why FrameNet contains potentially useful knowledge for coreference resolution, consider the following text segment: Peter Anthony decries program trading as “limiting the game to a few,” but he is not sure whether he wants to denounce it because . [sent-194, score-0.448]
75 To establish the coreference relation between it and program trading, it may be helpful to know that decry and denounce appear in the same frame and the two NPs have the same semantic role. [sent-197, score-0.474]
76 This example suggests that features encoding both the semantic roles of the two NPs under consideration and whether the associated predicates are “related” to each other in FrameNet (i. [sent-198, score-0.185]
77 , whether they appear in the same frame) could be useful for identifying coreference relations. [sent-200, score-0.308]
78 First, since we do not employ verb sense dis- ambiguation, we consider two predicates related as long as there is at least one semantic frame in which they both appear. [sent-202, score-0.194]
79 , 2004), a semantic role labeler that provides PropBank-style semantic roles such as ARG0 (the PROTOAGENT, which is typically the subject of a transitive verb) and ARG1 (the PROTOPATIENT, which is typically its direct object). [sent-204, score-0.119]
80 Now, assuming that NPj and NPk are the arguments of two stemmed predicates, predj and predk, we create 15 features using the knowledge extracted from FrameNet and ASSERT as follows. [sent-205, score-0.243]
81 First, we encode the knowledge extracted from FrameNet as one of three possible values: (1) predj and predk are in the same frame; (2) they are both predicates in FrameNet but never appear in the same frame; and (3) one or both predicates do not appear in FrameNet. [sent-206, score-0.291]
82 2 Finally, we create 15 binary-valued features by pairing the 3 possible values extracted from FrameNet and the 5 possible values provided by ASSERT. [sent-208, score-0.136]
83 If this assumption fails, we will not create any features based on FrameNet for these two NPs. [sent-215, score-0.089]
84 To our knowledge, FrameNet has not been exploited for coreference resolution. [sent-216, score-0.283]
85 2 World Knowledge from Annotated Data Since world knowledge is needed for coreference resolution, a human annotator must have employed world knowledge when coreference-annotating a document. [sent-219, score-0.659]
86 We aim to design features that can “recover” such world knowledge from annotated data. [sent-220, score-0.227]
87 1 Features Based on Noun Pairs A natural question is: what kind of world knowledge can we extract from annotated data? [sent-223, score-0.212]
88 president if we see these two NPs appearing in the same coreference chain. [sent-226, score-0.283]
89 Note that any features computed based on WordNet distance or distributional similarity are likely to incorrectly suggest that lion and tiger are coreferent, since the two nouns are similar distributionally and according to WordNet. [sent-229, score-0.086]
90 819 To improve generalization, we instead create different kinds of noun-pair-based features given an annotated text. [sent-232, score-0.089]
91 Next, we create noun-pair-based features for the MP model, which will be used to augment the Baseline feature set. [sent-238, score-0.113]
92 Either an UNSEEN-SAME feature or an UNSEEN-DIFF feature is created, depend- ing on whether the two NPs are the same string before being replaced with the UNSEEN token. [sent-241, score-0.099]
93 If exactly one of NPj and NPk is tagged as a NE by the Stanford NE recognizer, we create a semi-lexical feature that is identical to the lexical feature described above, except that the NE is replaced with its NE label. [sent-246, score-0.124]
94 Specifically, since each instance now corresponds to an NP, NPk, and a preceding cluster, c, we can generate a noun-pair-based feature by applying the above method to NPk and each of the NPs in c, and its value is the number of times it is applicable to NPk and c. [sent-255, score-0.127]
95 2 Features Based on Verb Pairs As discussed above, features encoding the semantic roles of two NPs and the relatedness of the associated verbs could be useful for coreference resolution. [sent-258, score-0.403]
96 Rather than encoding verb relatedness, we may replace verb relatedness with the verbs themselves in these features, and have the learner learn directly from coreference-annotated data whether two NPs serving as the objects of decry and denounce are likely to be coreferent or not, for instance. [sent-259, score-0.184]
97 Specifically, assuming that NPj and NPk are the arguments of two stemmed predicates, predj and predk, in the training data, we create five features as follows. [sent-260, score-0.16]
98 Second, we create five binary-valued features by pairing each of these five values with the two stemmed predicates. [sent-262, score-0.143]
99 If this assumption fails, we will not create any features based on verb pairs for these two NPs. [sent-268, score-0.089]
100 3 World Knowledge from Unannotated Data Previous work has shown that syntactic appositions, which can be extracted using heuristics from unannotated documents or parse trees, are a useful source of world knowledge for coreference resolution (e. [sent-270, score-0.634]
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