emnlp emnlp2010 emnlp2010-8 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Karthik Raghunathan ; Heeyoung Lee ; Sudarshan Rangarajan ; Nate Chambers ; Mihai Surdeanu ; Dan Jurafsky ; Christopher Manning
Abstract: Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features. This approach can lead to incorrect decisions as lower precision features often overwhelm the smaller number of high precision ones. To overcome this problem, we propose a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision. Each tier builds on the previous tier’s entity cluster output. Further, our model propagates global information by sharing attributes (e.g., gender and number) across mentions in the same cluster. This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available at the time. The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. In spite of its simplicity, our approach outperforms many state-of-the-art supervised and unsupervised models on several standard corpora. This suggests that sievebased approaches could be applied to other NLP tasks.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features. [sent-2, score-0.972]
2 To overcome this problem, we propose a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision. [sent-4, score-1.234]
3 , gender and number) across mentions in the same cluster. [sent-8, score-0.504]
4 This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available at the time. [sent-9, score-0.408]
5 The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. [sent-10, score-0.379]
6 1 Introduction Recent work on coreference resolution has shown that a rich feature space that models lexical, syntactic, semantic, and discourse phenomena is crucial to successfully address the task (Bengston and Roth, 2008; Haghighi and Klein, 2009; Haghighi and Klein, 2010). [sent-13, score-0.526]
7 By and large most approaches decide if two mentions are coreferent using a single function over all these features and information local to the two mentions. [sent-15, score-0.487]
8 This initial clustering step will assign the correct animacy attribute (inanimate) to the corresponding geo-political entity, which will prevent the incorrect merging with the mention we (animate) in later steps. [sent-25, score-0.462]
9 The approach applies tiers of coreference models one at a time from highest to lowest precision. [sent-30, score-0.408]
10 Furthermore, each model’s decisions are richly informed by sharing attributes across the mentions clustered in earlier tiers. [sent-32, score-0.56]
11 All our components are unsupervised, in the sense that they do not require training on gold coreference links. [sent-35, score-0.412]
12 Our approach outperforms most other unsupervised coreference models and several supervised ones on several datasets. [sent-39, score-0.416]
13 We believe that our approach also serves as an ideal platform for the development of future coreference systems. [sent-41, score-0.379]
14 Related Work This work builds upon the recent observation that strong features outweigh complex models for coreference resolution, in both supervised and unsupervised learning setups (Bengston and Roth, 2008; Haghighi and Klein, 2009). [sent-42, score-0.449]
15 Most coreference resolution approaches perform the task by aggregating local decisions about pairs of mentions (Bengston and Roth, 2008; Finkel and Manning, 2008; Haghighi and Klein, 2009; Stoyanov, 2010). [sent-44, score-0.921]
16 They perform coreference resolution jointly for all mentions in a document, using first-order probabilistic models in either supervised or unsupervised settings. [sent-47, score-0.958]
17 To the best of our knowledge, we are the first to apply this theory to coreference resolution. [sent-55, score-0.379]
18 3 Description of the Task Intra-document coreference resolution clusters together textual mentions within a single document based on the underlying referent entity. [sent-56, score-0.987]
19 To facilitate comparison with most of the recent previous work, we report results using gold mention boundaries. [sent-58, score-0.305]
20 , see Haghighi and Klein (2010) for a simple mention detection model). [sent-61, score-0.306]
21 The syntactic in- formation is used to identify the mention head words and to define the ordering of mentions in a given sentence (detailed in the next section). [sent-76, score-0.87]
22 For a fair comparison with previous work, we do not use gold named entity labels or mention types but, instead, take the labels provided by the Stanford named entity recognizer (NER) (Finkel et al. [sent-77, score-0.513]
23 2 Evaluation Metrics We use three evaluation metrics widely used in the literature: (a) pairwise F1 (Ghosh, 2003) computed over mention pairs in the same entity cluster; (b) MUC (Vilain et al. [sent-80, score-0.4]
24 – – – 4 Description of the Multi-Pass Sieve Our sieve framework is implemented as a succes- sion ofindependent coreference models. [sent-84, score-0.702]
25 1 Mention Processing Given a mention mi, each model may either decline to propose a solution (in the hope that one of the subsequent models will solve it) or deterministically select a single best antecedent from a list of previous mentions m1, . [sent-87, score-0.878]
26 If the – sentence containing the anaphoric mention contains multiple clauses, we repeat the above heuristic separately in each S * constituent, starting with the one containing the mention. [sent-95, score-0.314]
27 Previous Sentence For all nominal mentions we sort candidates in the previous sentences using rightto-left breadth-first traversal. [sent-96, score-0.513]
28 For example, this ordering favors the correct candidate (pepsi) for the mention they: [pepsi] says it expects to double [quaker]’s snack food growth rate. [sent-101, score-0.368]
29 – In a significant departure from previous work, each model in our framework gets (possibly incomplete) clustering information for each mention from the earlier coreference models in the multi-pass system. [sent-105, score-0.651]
30 In other words, each mention mi may already be assigned to a cluster Cj containing a set of mentions: Cj = ,. [sent-106, score-0.399]
31 Unassigned {mj1 mkj}; mentions are unique memb};er ms of∈ ∈th Ceir own cluster. [sent-110, score-0.436]
32 fected by missing attributes (which introduce precision errors because incorrect antecedents are selected due to missing information) and incorrect attributes (which introduce recall errors because correct links are not generated due to attribute mismatch between mention and antecedent). [sent-113, score-0.861]
33 To address this issue, we perform a union of all mention attributes (e. [sent-114, score-0.396]
34 If attributes from different mentions contradict each other we maintain all variants. [sent-117, score-0.56]
35 For example, our naive number detection assigns s ingular to the mention a group of students and plural to five students. [sent-118, score-0.401]
36 When these mentions end up in the same cluster, the resulting number attributes becomes the set {s ingular, plural}. [sent-119, score-0.56]
37 Mention selection Traditionally, a coreference model attempts to resolve every mention in the text, which increases the likelihood of errors. [sent-121, score-0.651]
38 Instead, in each of our models, we exploit the cluster information received from the previous stages by resolving only mentions that are currently first in textual order in their cluster. [sent-122, score-0.563]
39 These two are the only mentions that have potential antecedents and are currently marked as the first mentions in their clusters. [sent-124, score-0.92]
40 First, early cluster mentions are usually better defined than subsequent ones, which are likely to have fewer modifiers or are pronouns (Fox, 495 1993). [sent-126, score-0.725]
41 Second, by definition, first mentions appear closer to the beginning of the document, hence there are fewer antecedent candidates to select from, and fewer opportunities to make a mistake. [sent-128, score-0.606]
42 We disable coreference for first cluster mentions that: (a) are or start with indefinite pronouns (e. [sent-130, score-1.053]
43 One exception to this rule is the model deployed in the first pass; it only links mentions if their entire extents match exactly. [sent-135, score-0.527]
44 This model is triggered for all nominal mentions regardless of discourse salience, because it is possible that indefinite mentions are repeated in a document when concepts are discussed but not instantiated, e. [sent-136, score-1.041]
45 2 The Modules of the Multi-Pass Sieve We now describe the coreference mented in the sieve. [sent-145, score-0.379]
46 For clarity, we in Table 1 and show the cumulative they are added to the sieve in Table models implesummarize them performance as 2. [sent-146, score-0.323]
47 1 Pass 1- Exact Match This model links two mentions only if they contain exactly the same extent text, including modifiers and determiners, e. [sent-149, score-0.588]
48 2 Pass 2 - Precise Constructs This model links two mentions if any of the conditions below are satisfied: Appositive – the two nominal mentions are in an appositive construction, e. [sent-155, score-1.072]
49 The Type column indicates the type of coreference in each pronominal. [sent-162, score-0.379]
50 or P – Predicate nominative the two mentions (nominal or pronominal) are in a copulative subject-object relation, e. [sent-164, score-0.47]
51 Role appositive the candidate antecedent is headed by a noun and appears as a modifier in an NP whose head is the current mention, e. [sent-167, score-0.403]
52 This feature is inspired by Haghighi and Klein (2009), who triggered it only if the mention is labeled as a person by the NER. [sent-170, score-0.316]
53 We constrain this heuristic more in our work: we allow this feature to match only if: (a) the mention is labeled as a person, (b) the antecedent is animate – – (we detail animacy detection in Pass 7), and (c) the antecedent’s gender is not neutral. [sent-171, score-0.79]
54 Relative pronoun the mention is a relative pronoun that modifies the head of the antecedent NP, e. [sent-172, score-0.714]
55 Acronym both mentions are tagged as NNP and one of them is an acronym of the other, e. [sent-175, score-0.555]
56 We use a simple acronym detection algorithm, which marks a mention as an acronym of another if its text equals the sequence of upper case characters in the other mention. [sent-181, score-0.544]
57 Demonym one of the mentions is a demonym of the other, e. [sent-183, score-0.521]
58 As shown in Table 2 the pairwise precision of the sieve – – – 3 3 http :/ / en . [sent-191, score-0.419]
59 3 Pass 3 - Strict Head Matching Linking a mention to an antecedent based on the naive matching of their head words generates a lot of spurious links because it completely ignores possibly incompatible modifiers (Elsner and Charniak, 2010). [sent-196, score-0.753]
60 To address this issue, this pass implements several features that must all be matched in order to yield a link: Cluster head match the mention head word matches any head word in the antecedent cluster. [sent-198, score-1.005]
61 Note that this feature is actually more relaxed than naive head matching between mention and antecedent candidate because it is satisfied when the mention’s head matches the head of any entity in the candidate’s cluster. [sent-199, score-0.959]
62 – Word inclusion all the non-stop4 words in the mention cluster are included in the set of non-stop words in the cluster of the antecedent candidate. [sent-201, score-0.696]
63 This heuristic exploits the property of discourse that – it is uncommon to introduce novel information in later mentions (Fox, 1993). [sent-202, score-0.519]
64 Typically, mentions of the same entity become shorter and less informative as the narrative progresses. [sent-203, score-0.504]
65 does look like very dramatic change made by [the Florida court] point to the same entity, but the two mentions in the text below belong to different clusters: The pilot had confirmed . [sent-210, score-0.436]
66 Compatible modifiers only the mention’s modifiers are all included in the modifiers of the antecedent candidate. [sent-215, score-0.476]
67 This feature models the same discourse property as the previous feature, but it fo– cuses on the two individual mentions to be linked, rather than their entire clusters. [sent-216, score-0.477]
68 Not i-within-i the two mentions are not in an iwithin-i construct, i. [sent-218, score-0.436]
69 5 Pass 6 - Relaxed Head Matching This pass relaxes the cluster head match heuristic by allowing the mention head to match any word in the cluster of the candidate antecedent. [sent-232, score-1.072]
70 For example, this heuristic matches the mention Sanders to a cluster containing the mentions {Sauls, the judge, Cluisrtceurit c Judge Nin. [sent-233, score-0.877]
71 6 Pass 7 - Pronouns With one exception (Pass 2), all the previous coreference models focus on nominal coreference resolution. [sent-241, score-0.835]
72 However, it would be incorrect to say that our framework ignores pronominal coreference in the first six passes. [sent-242, score-0.508]
73 In fact, the previous models prepare the stage for pronominal coreference by constructing precise clusters with shared mention attributes. [sent-243, score-0.854]
74 Like previous work, we implement pronominal coreference resolution by enforcing agreement constraints between the coreferent mentions. [sent-245, score-0.656]
75 We exclude from this analysis two notable works that report results only on a version of the task that includes finding mentions (Haghighi and Klein, 2010; Stoyanov, 2010). [sent-267, score-0.436]
76 Our sieve model outperforms all systems on two out of the four evaluation corpora (ACE2004ROTH-DEV and ACE2004-NWIRE), on all metrics. [sent-270, score-0.323]
77 We believe this is particularly useful for large-scale NLP applications that use coreference resolution components, e. [sent-278, score-0.485]
78 These applications can generally function without coreference information so it is beneficial to provide such information only when it is highly precise. [sent-281, score-0.379]
79 1 Comparison to Previous Work The sieve model outperforms all other systems on at least two test sets, even though most of the other models are significantly richer. [sent-283, score-0.323]
80 2 Semantic Head Matching Recent unsupervised coreference work Haghighi and Klein (2009) included a semantic component that matched related words (e. [sent-298, score-0.416]
81 They first identified articles relevant to the entity mentions in the test set, and then bootstrapped from known syntactic patterns for apposition and predicate-nominatives in order to learn a database of related head pairs. [sent-372, score-0.634]
82 They show impressive gains by using these learned pairs in coreference decisions. [sent-373, score-0.379]
83 This type of learning using test set mentions is often described as transductive. [sent-374, score-0.436]
84 However, our results show 499 that our sieve algorithm with minimal semantic information still performs as well as the Haghighi and Klein (2009) system with semantics. [sent-377, score-0.323]
85 3 Flexible Architecture The sieve architecture offers benefits beyond improved accuracy. [sent-379, score-0.389]
86 The sieve allows new features to be seamlessly inserted without affecting (or even understanding) the other components. [sent-381, score-0.357]
87 It can be difficult to fully understand how a system makes a single decision, but the sieve allows for flexible usage with minimal effort. [sent-384, score-0.323]
88 For example, the combined (precision plus recall) number of errors for proper or common noun mentions is three times larger than the number of errors made for pronom- inal mentions. [sent-392, score-0.534]
89 In order to understand the limitations of our current system, we randomly selected 60 recall errors (20 for each mention type) and investigated their causes. [sent-397, score-0.321]
90 For proper nouns, 50% of recall errors are due to mention lengthening, mentions that are longer than their earlier mentions. [sent-399, score-0.757]
91 For example, Washingtonbased USAir appears after USAir in the text, so our head matching components skip it because their high precision depends on disallowing new modifiers as the discourse proceeds. [sent-400, score-0.338]
92 When the mentions were reversed (as is the usual case), they match. [sent-401, score-0.436]
93 Our sieve-based approach to coreference uniquely allows for such new models to be seamlessly inserted. [sent-415, score-0.413]
94 7 Conclusion We presented a simple deterministic approach to coreference resolution that incorporates documentlevel information, which is typically exploited only by more complex, joint learning models. [sent-416, score-0.543]
95 Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model’s cluster output. [sent-417, score-0.986]
96 An additional benefit of the sieve framework is its modularity: new features or models can be inserted in the system with limited understanding of the other features already deployed. [sent-419, score-0.323]
97 Our code is publicly released5 and can be used both as a stand-alone coreference system and as a platform for the development of future systems. [sent-420, score-0.379]
98 Simple coreference resolution with rich syntactic and semantic features. [sent-507, score-0.485]
99 Gender and animacy knowledge discovery from web-scale n-grams for unsupervised person mention detection. [sent-525, score-0.472]
100 Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art. [sent-579, score-0.379]
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