acl acl2013 acl2013-352 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Xingxing Zhang ; Jianwen Zhang ; Junyu Zeng ; Jun Yan ; Zheng Chen ; Zhifang Sui
Abstract: Distant supervision (DS) is an appealing learning method which learns from existing relational facts to extract more from a text corpus. However, the accuracy is still not satisfying. In this paper, we point out and analyze some critical factors in DS which have great impact on accuracy, including valid entity type detection, negative training examples construction and ensembles. We propose an approach to handle these factors. By experimenting on Wikipedia articles to extract the facts in Freebase (the top 92 relations), we show the impact of these three factors on the accuracy of DS and the remarkable improvement led by the proposed approach.
Reference: text
sentIndex sentText sentNum sentScore
1 Abstract Distant supervision (DS) is an appealing learning method which learns from existing relational facts to extract more from a text corpus. [sent-6, score-0.379]
2 In this paper, we point out and analyze some critical factors in DS which have great impact on accuracy, including valid entity type detection, negative training examples construction and ensembles. [sent-8, score-0.799]
3 By experimenting on Wikipedia articles to extract the facts in Freebase (the top 92 relations), we show the impact of these three factors on the accuracy of DS and the remarkable improvement led by the proposed approach. [sent-10, score-0.344]
4 They are composed of relational facts often represented in the form of a triplet, ( S rcEnt ity Re l i at on D stEnt ity ) , such as “(Bill Gates, BornIn, Seattle)”. [sent-12, score-0.334]
5 An important task is to enrich such KBs by extracting more facts from text. [sent-13, score-0.219]
6 Specifically, this paper focuses on extracting facts for existing relations. [sent-14, score-0.219]
7 However, it is difficult to handle large scale corpus due to the high cost of labeling. [sent-19, score-0.042]
8 Recently an approach called distant supervision (DS) (Mintz et al. [sent-20, score-0.221]
9 It treats the extraction problem as classifying , ∗ The contact author. [sent-22, score-0.084]
10 Then an existing fact in a KB can be used as a labeled example whose label is the relation name. [sent-24, score-0.229]
11 Then the features of all the sentences (from a given text corpus) containing the entity pair are merged as the feature of the example. [sent-25, score-0.393]
12 They argue that DS introduces a lot of noise into the training data by merging the features of all the sentences containing the same entity pair, because a sentence containing the entity pair of a relation may not talk about the relation. [sent-32, score-1.042]
13 (201 1) introduce hidden variables to indicate whether a sentence is noise and try to infer them from the data. [sent-35, score-0.062]
14 (2012) design a generative model to identify noise patterns. [sent-37, score-0.062]
15 In this paper, we point out and analyze some critical factors in DS which have great impact on the accuracy but has not been touched or well handled before. [sent-39, score-0.213]
16 First, each relation has its own schema definition, i. [sent-40, score-0.234]
17 , the source entity and the destination entity should be of valid types, which is overlooked in DS. [sent-42, score-1.096]
18 Therefore, we propose a component of entity type detection to check it. [sent-43, score-0.575]
19 Second, DS introduces many false negative examples into the training set and we propose a new method to construct negative training examples. [sent-44, score-0.329]
20 Third, we find it is difficult for a single classifier to achieve high accuracy and hence we train multiple classifiers and ensemble them. [sent-45, score-0.271]
21 We also notice that Nguyen and Moschitti (201 1a) and Nguyen and Moschitti (201 1b) utilize external information such as more facts from Yago and labeled sentences from ACE to improve the 810 Proce dingSsof oifa, th Beu 5l1gsarti Aan,An u aglu Mste 4e-ti9n2g 0 o1f3 t. [sent-46, score-0.283]
22 2 Critical Factors Affecting the Accuracy DS has four steps: (1) Detect candidate entity pairs in the corpus. [sent-50, score-0.432]
23 Among these steps, we find the following three critical factors have great impact on the accuracy (see Section 4 for the experimental results). [sent-54, score-0.184]
24 In DS, a sentence with a candidate entity pair a sentence with two candidate entities is noisy. [sent-56, score-0.646]
25 First, the schema of each relation in the KB requires that the source and destination entities should be of valid types, e. [sent-57, score-0.719]
26 , the source and destination entity of the relation “DirectorOfFilm” should be of the types “Director” and “Film” respectively. [sent-59, score-0.903]
27 If the two entities in a sentence are not of the valid types, the sentence is noisy. [sent-60, score-0.18]
28 Second, the sentence may not talk about the relation even when the two entities are of the valid types. [sent-61, score-0.379]
29 , 2012) do not distinguish the two types of noise but directly infer the overall noise from the data. [sent-65, score-0.166]
30 We argue that the first type ofnoise is very difficult to be inferred just from the noisy relational labels. [sent-66, score-0.119]
31 Instead, we decouple the two types of noise, and utilize external labeled data, i. [sent-67, score-0.106]
32 , the Wikipedia anchor links, to train an entity type detection module to handle the first type of noise. [sent-69, score-0.714]
33 We notice that when Ling and Weld (2012) studied a fine-grained NER method, they applied the method to relation extraction by adding the recognized entity tags to the features. [sent-70, score-0.708]
34 We worry that the contribution of the entity type features may be drowned when many other features are used. [sent-71, score-0.423]
35 DS treats the relation extraction as a multi-class classification task. [sent-75, score-0.283]
36 For a relation, it implies that the facts of all the other relations together with the “Other” class are negative examples. [sent-76, score-0.376]
37 This introduces many false negative examples into the training data. [sent-77, score-0.228]
38 First, many relations are not exclusive with each other, e. [sent-78, score-0.056]
39 Second, in DS, the “Other” class is composed of all the candidate entity pairs not existed in the KB, which actually contains many positive facts of non-Other relations because the KB is not complete. [sent-81, score-0.707]
40 Therefore we use a different way to construct negative training examples. [sent-82, score-0.101]
41 The features used in DS are very sparse and many examples do not contain any features. [sent-84, score-0.052]
42 However we find it is difficult for a single classifier on all the features to achieve high accuracy and hence we divide the features into different categories and train a separate classifier for each category and then ensemble them finally. [sent-86, score-0.26]
43 3 Accurate Distant Supervision (ADS) Different from DS, we treat the extraction problem as N binary classification problems, one for each relation. [sent-87, score-0.053]
44 In step (1), when detecting candidate entity pairs in sentences, we use our entity type detection module (Section 3. [sent-89, score-0.94]
45 1) to filter out the sentences where the entity pair is of invalid entity types. [sent-90, score-0.75]
46 In step (2), we use our new method to construct negative examples (Section 3. [sent-91, score-0.153]
47 In step (3), we employ more features and design an ensemble classifier (Section 3. [sent-93, score-0.189]
48 In step (4), we train N binary classifiers separately. [sent-95, score-0.083]
49 1 Entity Type Detection We divide the entity type detection into two steps. [sent-97, score-0.508]
50 The first step, called boundary detection, is to detect phrases as candidate entities. [sent-98, score-0.167]
51 The second step, called named entity disambiguation, maps a detected candidate entity to some entity types, e. [sent-99, score-1.146]
52 Note that an entity might be mapped to multiple types. [sent-102, score-0.357]
53 Boundary Detection Two ways are used for boundary detection. [sent-104, score-0.055]
54 First, for each relation, from the training set of facts, we get two dictionaries (one for source entities and one for destination entities). [sent-105, score-0.408]
55 The two dictionaries are used to detect the source and destination entities. [sent-106, score-0.342]
56 We first find the compatible NER tags for an entity type in the KB. [sent-108, score-0.51]
57 For example, 811 for the type “FilmDirector”, the compatible NER tag of Standford NER is “Person”. [sent-109, score-0.111]
58 To do this, for each entity type in the KB, we match all the entities of that type (in the training set) back to the training corpus and get the probability Ptag(ti) of each NER tag (including the “NULL” tag meaning not recognized as a named entity) recognized by the NER tool. [sent-110, score-0.706]
59 nd Ifid thaete eetnatiitnieeds recognized by NER tool will be discarded. [sent-115, score-0.057]
60 Named Entity Disambiguation (NED) With a candidate entity obtained by the boundary detection, we need a NED component to assign some entity types to it. [sent-116, score-0.917]
61 To obtain such a NED, we leverage the anchor text in Wikipedia to generate training data and train a NED component. [sent-117, score-0.098]
62 The referred Freebase entity and the types of an anchor link in Wikipedia can be obtained from Freebase. [sent-118, score-0.456]
63 2 Negative Examples Construction Treating the problem as a multi-class classification implies introducing many false negative examples for a relation; therefore, we handle each relation with a separate binary classifier. [sent-123, score-0.438]
64 However, a KB only tells us which entity pairs belong to a relation, i. [sent-124, score-0.357]
65 But we also need negative examples to train a binary classifier. [sent-127, score-0.194]
66 To reduce the number of false negative examples, we propose a new method to construct negative examples by utilizing the 1-to-1/1-to-n/n-to-1/n-to-n property of a relation. [sent-128, score-0.298]
67 1-to-1/n-to-1/1-to-n Relation A 1-to-1 or n-to1 relation is a functional relation: for a relation r, for each valid source entity e1, there is only one unique destination entity e2 such that (e1, e2) ∈ r. [sent-129, score-1.494]
68 Inequality (2) says the proportion of source entities which have exactly one counterpart destination entity should be greater than a given threshold. [sent-133, score-0.8]
69 Inequality (3) says the average number of destination entities of a source entity should be less than the threshold. [sent-134, score-0.8]
70 To check whether r is a 1-to-n relation, we simply swap the source and destination entities of the relation and check whether the reversed relation is a n-to-1 relation by the above two inequalities. [sent-135, score-1.242]
71 We approximately categorize a n-to-n relation to n-to-1 or 1-to-n by checking which one it is closer to. [sent-149, score-0.199]
72 This is done by computing the following two values αsrc and r is treated as a 1-to-n relation if αsrc > and as a 1-to-n relation otherwise. [sent-150, score-0.398]
73 yp(a4ir) (e1, e2) not in the relation r of the KB, we first determine whether it is 1-to-n or n-to-1 using the above method. [sent-154, score-0.199]
74 If r is 1-to-1/n-to-1 and e1 exists in some fact of r as the source entity, then (e1, e2) is a negative example as it violates the 1-to-1/n-to-1 constraint. [sent-155, score-0.149]
75 If r is 1-to-n, the judgement is similar and just simply swap the source and destination entities of the relation. [sent-156, score-0.448]
76 , 2011): Trigger Words (the words on the dependency path except stop words) and Entity String (source entity and destination entity). [sent-161, score-0.614]
77 We find that without considering the reversed order of entity pairs in a sentence, the precision can be higher, but the recall decreases. [sent-165, score-0.516]
78 For example, for the entity pair ⟨Ventura Pons, Actrius⟩, we only ocorn thsied eenr sentences ewnittuhr ath Peo right octrrdiuesr (e. [sent-166, score-0.393]
79 For each relation, we train four classifiers: C1 (without considering reversed order), C2 (considering reversed order), C1more (without considering reversed order and employ more feature) and C2more (considering reversed order and employ more feature). [sent-170, score-0.687]
80 We then ensemble the four classifiers by averaging the probabilities of predictions: P(y|x) 4 = P1+ P2+ P14more+ P2more (5) Experiments 4. [sent-171, score-0.161]
81 1 Dataset and Configurations We aimed to extract facts of the 92 most frequent relations in Freebase 2009. [sent-172, score-0.275]
82 The facts of each relation were equally split to two parts for training and testing. [sent-173, score-0.418]
83 During the NED phrase, there are 94 unique entity types (they are also relations in Freebase) for the source and destination entities. [sent-175, score-0.76]
84 Note that some entity types contain too few entities and they are discarded. [sent-176, score-0.502]
85 , 2008) as classifiers for NED and relation extraction and the solver is L2LR. [sent-182, score-0.294]
86 We evaluate the performance on the half hold-on facts for testing. [sent-185, score-0.219]
87 We compared performance of the n = 50, 000 best extracted relation instances of each method and the Precision-Recall (PR) curves are in Figure 1 and Recall Figure 1: Performance of different methods. [sent-186, score-0.199]
88 For a candidate fact without any entity existing in Freebase, we are not able to judge whether it is correct. [sent-189, score-0.432]
89 Thus we only evaluate the candidate facts that at least one entity occurs as the source or destination entity in the test fact set. [sent-190, score-1.313]
90 , 2012), we selected the top 50 ranked (according to their classification probabilities) relation facts of the 15 largest relations. [sent-200, score-0.418]
91 3 Contribution of Each Component In Figure 2, with the entity type detection (ETD), the performance is better than the original DS method (OrigDS). [sent-212, score-0.508]
92 ETD is also better than adding the entity types of the pair to the feature vector (DS Figer)2 as in (Ling and Weld, 2012). [sent-214, score-0.435]
93 If we also employ the negative example construction strategy in Section 3. [sent-215, score-0.14]
94 By adding more features (More) and employing the ensemble learning (Ensemble(ADS)) to ETD+Neg, the performance is further improved. [sent-217, score-0.119]
95 We find some factors are crucially important, including valid entity type detection, negative training examples construction and ensembles. [sent-219, score-0.706]
96 2We use Figer (Ling and Weld, 2012) to detect entity types Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, XiangRui Wang, and Chih-Jen Lin. [sent-237, score-0.436]
97 Incorporating non-local information into information extraction systems by gibbs sampling. [sent-243, score-0.053]
98 Knowledge-based weak supervision for information extraction of overlapping relations. [sent-249, score-0.16]
99 End-to-end relation extraction using distant supervision from external semantic repositories. [sent-267, score-0.507]
100 Reducing wrong labels in distant supervision for relation extraction. [sent-285, score-0.42]
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