Paper:
Transductive Non-linear Learning for Chinese Hypernym Prediction
Finding the correct hypernyms for entities is essential for taxonomy learning, finegrained entity categorization, knowledge base construction, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.
1 Introduction
A hypernym of an entity characterizes the type or the class of the entity. For example, the word country is the hypernym of the entity Canada. The accurate prediction of hypernyms benefits a variety of NLP tasks, such as taxonomy learning (Wu et al., 2012; Fu et al., 2014), fine-grained entity categorization (Ren et al., 2016), knowledge base construction (Suchanek et al., 2007), etc.
In previous work, the detection of hypernyms requires lexical, syntactic and/or semantic analysis of relations between entities and their respective hypernyms from a language-specific knowledge source. For example, Hearst (1992) is the pioneer work to extract is-a relations from a text corpus based on handcraft patterns. The followingup work mostly focuses on is-a relation extraction using automatically generated patterns (Snow et al., 2004; Ritter et al., 2009; Sang and Hof-
mann, 2009; Kozareva and Hovy, 2010) and relation inference based on distributional similarity measures (Kotlerman et al., 2010; Lenci and Benotto, 2012; Shwartz et al., 2016).
While these approaches have relatively high precision over English corpora, extracting hypernyms for entities is still challenging for Chinese. From the linguistic perspective, Chinese is a lower-resourced language with very flexible expressions and grammatical rules (Wang et al., 2015). For instance, there are no word spaces, explicit tenses and voices, and distinctions between singular and plural forms in Chinese. The order of words can be changed flexibly in sentences. Hence, as previous research indicates, hypernym extraction methods for English are not necessarily suitable for the Chinese language (Fu et al., 2014; Wang et al., 2015; Wang and He, 2016).
Based on such conditions, several classification methods are proposed to distinguish is-a and notis-a relations based on Chinese encyclopedias (Lu et al., 2015; Li et al., 2015). Similar to Princeton WordNet, a few Chinese wordnets have also been developed (Huang et al., 2004; Xu et al., 2008; Wang and Bond, 2013). The most recent approaches for Chinese is-a relation extraction (Fu et al., 2014; Wang and He, 2016) use word embedding based linear projection models to map embeddings of hyponyms to those of their hypernyms, which outperform previous algorithms.
However, we argue that these projection-based methods may have three potential limitations: (i) Only positive is-a relations are used for projection learning. The distinctions between is-a and not-is-a relations in the embedding space are not modeled. (ii) These methods lack the capacity to encode linguistic rules, which are designed by linguists and usually have high precision. (iii) It assumes that the linguistic regularities of is-a relations can be solely captured by single or multiple
2
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
linear projection models. In this paper, we address these limitations by a two-stage transductive learning approach. It distinguishes is-a and not-is-a relations given a Chinese word/phrase pair as input. In the initial stage, we train linear projection models on positive and negative training data separately and predict isa relations jointly. In the transductive learning stage, the initial prediction results, linguistic rules and the non-linear mappings from entities to hypernyms are optimized simultaneously in a unified framework. This optimization problem can be efficiently solved by blockwise gradient descent. We evaluate our method over two public datasets and show that it outperforms state-of-the-art approaches for Chinese hypernym prediction.1
The rest of this paper is organized as follows. We summarize the related work in Section 2. Our approach is introduced in Section 3. Experimental results are presented in Section 4. We conclude our paper in Section 5.2
2 Related Work
In this section, we overview the related work on hypernym prediction and discuss the challenges of Chinese hypernym detection.
Pattern based methods identify is-a relations from texts by handcraft or automatically generated patterns. Hearst patterns (Hearst, 1992) are lexical patterns in English that are employed to extract isa relations for taxonomy construction (Wu et al., 2012). Automatic approaches mostly use iterative learning paradigms such that the system learns new is-a relations and patterns simultaneously. A few relevant studies can be found in (Caraballo, 1999; Etzioni et al., 2004; Sang, 2007; Pantel and Pennacchiotti, 2006; Kozareva and Hovy, 2010). To avoid “semantic drift” in iterations, Snow et al. (2004) train a hypernym classifier based on syntactic features based on parse trees. Carlson et al. (2010) exploit multiple learners to extract relations via coupled learning. These approaches are
1While there is abundant research on hypernym prediction for English with high precision, we mostly focus on the Chinese language in this paper. However, the proposed method is not entirely language-specific and has the potential to be adapted to other languages. In Section 4, we provide additional experiments to show that our approach also outperforms several existing methods for hypernym prediction in the English environment. We also discuss some potential applications of our method.
2Additionally, we present more details on algorithms and experimental settings and a prototype system for taxonomy visualization in the supplementary notes.
not effective for Chinese for two reasons: i) Chinese is-a relations are expressed in a highly flexible manner (Fu et al., 2014) and ii) the accuracy of basic NLP tasks such as dependency parsing still need improvement for Chinese (Li et al., 2013).
Inference based methods take advantage of distributional similarity measures (DSM) to infer relations between words. They assume that a hypernym may appear in all contexts of the hyponyms and a hyponym can only appear in part of the contexts of its hypernyms. In previous work, Kotlerman et al. (2010) design directional DSMs to model the asymmetric property of is-a relations. Other DSMs are introduced in (Bhagat et al., 2007; Szpektor et al., 2007; Lenci and Benotto, 2012; Santus et al., 2014). Shwartz et al. (2016) combine dependency parsing and DSM to improve the performance of hypernymy detection. The reason why DSM is not effective for Chinese is that the contexts of entities in Chinese are flexible and sparse.
Encyclopedia based methods take encyclopedias as knowledge sources to construct taxonomies. Ponzetto and Strube (2007) design features from multiple aspects to predict is-a relations between entities and categories in English Wikipedia. The taxonomy in YAGO (Suchanek et al., 2007) is constructed by linking conceptual categories in Wikipedia to WordNet synsets (Miller, 1995). For Chinese, Li et al. (2015) propose an SVM-based approach to build a large Chinese taxonomy from Wikipedia. Similar classification based algorithms are presented in (Fu et al., 2013; Lu et al., 2015). Due to the lack of Chinese version of WordNet, several Chinese semantic dictionaries have been conducted, such as Sinica BOW (Huang et al., 2004), SEW (Xu et al., 2008), COW (Wang and Bond, 2013), etc. These approaches have higher accuracy than mining hypernym relations from texts directly. However, they heavily rely on existing knowledge sources and are difficult to extend to different domains.
To tackle these challenges, word embedding based methods directly model the task of hypernym prediction as learning a mapping from entity vectors to their respective hypernym vectors in the embedding space. The vectors can be pretrained by neural language models (Mikolov et al., 2013). For the Chinese language, Fu et al. (2014) train piecewise linear projection models based on a Chinese thesaurus. The state-of-the-art method
3
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
(Wang and He, 2016) combines an iterative learning procedure and Chinese Hearst-style patterns to improve the performance of projection models. They can reduce data noise by avoiding direct parsing of Chinese texts, but still capture the linguistic regularities of is-a relations based on word embeddings. Additionally, several work aims to study how to combine word embeddings for relation classification, such as (Mirza and Tonelli, 2016). In our paper, we extend these approaches by modeling non-linear mappings from entities to hypernyms and adding linguistic rules via a unified transductive learning framework.
3 Proposed Approach
This section begins with a brief overview of our approach. After that, the detailed steps and the learning algorithm are introduced in detail.
3.1 Overview
Given a word/phase pair (xi, yi), the goal of our task is to learn a classification model to predict whether yi is the hypernym of xi.
As illustrated in Figure 1, our approach has two stages: initial stage and transductive learning stage. The input is a positive is-a set D+, a negative is-a set D− and an unlabeled set DU , all of which are the collections of word/phrase pairs.
Denote xi as the embedding vector of word xi, pre-trained and stored in a lookup table. In the initial stage, we train a linear projection model over D+ such that for each (xi, yi) ∈ D+, a projection matrix maps the entity vector xi to its hypernym vector yi. A similar model is also trained over D−. Based on the two models, we estimate the prediction score and the confidence score for each (xi, yi) ∈ DU . In the transductive learning stage, a joint optimization problem is formed to learn the final prediction score for each (xi, yi) ∈ DU . It aims to minimize the prediction errors based on the human labeled data, the initial model prediction and linguistic rules. It also employs nonlinear mappings to capture linguistic regularities of is-a relations other than linear projections.
3.2 Initial Model Training
The initial stage models how entities are translated to their hypernyms or non-hypernyms by projection learning. We first train a Skip-gram model (Mikolov et al., 2013) to learn word embeddings over a large text corpus. Inspired by
(Fu et al., 2014; Wang and He, 2016), for each (xi, yi) ∈ D+, we assume there is a positive projection model such that M+xi ≈ yi where M+ is an |xi| × |xi| projection matrix3. However, this model does not capture the semantics of not-isa relations. Thus, we learn a negative projection model M−xi ≈ yi where (xi, yi) ∈ D−. This approach is equivalent to learning two separate translation models within the same semantic space. For parameter estimation, we minimize the two following objectives:
J(M+) = 1
2 ∑ (xi,yi)∈D+ ‖M+xi−yi‖22+ λ 2 ‖M+‖2F
J(M−) = 1
2 ∑ (xi,yi)∈D− ‖M−xi−yi‖22+ λ 2 ‖M−‖2F
where λ > 0 is a Tikhonov regularization parameter (Golub et al., 1999).
In the testing phase, for each (xi, yi) ∈ DU , denote d+(xi, yi) = ‖M+xi − yi‖2 and d−(xi, yi) = ‖M−xi−yi‖2. The prediction score is defined as:
score(xi, yi) = tanh(d −(xi, yi)− d+(xi, yi))
where score(xi, yi) ∈ (−1, 1). Higher prediction score indicates there is a larger probability of an is-a relation between xi and yi. We choose the hyperbolic tangent function rather than the sigmoid function to avoid the widespread saturation of sigmoid function (Menon et al., 1996).
The difference between d+(xi, yi) and d−(xi, yi) can be also used to indicate whether the models are confident enough to make a prediction. In this paper, we calculate the confidence score as:
conf(xi, yi) = |d+(xi, yi)− d−(xi, yi)| max{d+(xi, yi), d−(xi, yi)} 3We have also examined piecewise linear projection models proposed in (Fu et al., 2014; Wang and He, 2016) as the initial models for transductive learning. However, we found that this practice is less efficient and the performance does not improve significantly.
4
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
where conf(xi, yi) ∈ (0, 1). Higher confidence score means that there is a larger probability that the models can predict whether there is an is-a relation between xi and yi correctly. This score gives different data instances different weights in the transductive learning stage.
3.3 Transductive Non-linear Learning
Although linear projection methods are effective for Chinese hypernym prediction, it does not encode non-linear transformation and only leverages the positive data. We present an optimization framework for non-linear mapping utilizing both labeled and unlabeled data and linguistic rules by transductive learning (Gammerman et al., 1998; Chapelle et al., 2006).
Let Fi be the final prediction score of the word/phrase pair (xi, yi). In the initialization stage of our algorithm, we set Fi = 1 if (xi, yi) ∈ D+, Fi = −1 if (xi, yi) ∈ D− and set Fi randomly in (−1, 1) if (xi, yi) ∈ DU . In matrix representation, denote F as the m× 1 final prediction vector where m = |D+| + |D−| + |DU |. Fi is the ith element in F. The three components in our transductive learning model are as follows:
3.3.1 Initial Prediction
Denote S as an m×1 initial prediction vector. We set Si = 1 if (xi, yi) ∈ D+, Si = −1 if (xi, yi) ∈ D− and Si = score(xi, yi) if (xi, yi) ∈ DU . In order to encode the confidence of model prediction, we define W as an m ×m diagonal weight matrix. The element in the ith row and the jth column of W is set as follows:
Wi,j =  conf(xi, yi) i = j, (xi, yi) ∈ DU 1 i = j, (xi, yi) ∈ D+ ∪D−
0 Otherwise
The objective function is defined as: Os = ‖W(F−S)‖22, which encodes the hypothesis that the final prediction should be similar to the initial prediction for unlabeled data or human labeling for training data. The weight matrix W gives the largest weight (i.e., 1) to all the pairs in D+ ∪D− and a larger weight to the pair (xi, yi) ∈ DU if the initial prediction is more confident.
3.3.2 Linguistic Rules
Although linguistic rules can only cover a few circumstances, they are effective to guide the learning process. For Chinese hypernym prediction,
Li et al. (2015) study the word formation of conceptual categories in Chinese Wikipedia. In our model, let C be the collection of linguistic rules. γi is the true positive (or negative) rate with respect to the respective positive (or negative) rule ci ∈ C, estimated over the training set. Considering the word formation of Chinese entities and hypernyms, we design one positive rule (i.e., P1) and two negative rules (i.e., N1 and N2), shown in Table 1.
Let R be an m × 1 linguistic rule vector and Ri is the ith element in R. For training data, we set Ri = 1 if (xi, yi) ∈ D+ and Ri = −1 if (xi, yi) ∈ D−, which follows the same settings as those in S. For unlabeled pairs that do not match any linguistic rules in C, we update Ri = Fi in each iteration of the learning process, meaning no loss for errors imposed in this part.
For other conditions, denote C(xi,yi) ⊆ C as the collection of rules that (xi, yi) matches. If C(xi,yi) are positive rules, we set Ri as follows:
Ri = max{Fi, max cj∈C(xi,yi) γj}
Similarly, if C(xi,yi) are negative rules, we have:
Ri = −max{−Fi, max cj∈C(xi,yi) γj}
which means Fi receives a penalty only if Fi < maxcj∈C(xi,yi) γj for pairs that match positive rules or Fi > −maxcj∈C(xi,yi) γj for negative rules4. The objective function is: Or = ‖F−R‖22. In this way, our model can integrate arbitrary “soft” constraints, making it robust to false positives or negatives introduced by these rules.
3.3.3 Non-linear Learning
TransLP is a transductive label propagation framework (Liu and Yang, 2015) for link prediction, previously used for applications such as text classification (Xu et al., 2016). In our work, we extend their work for our task, modeling non-linear mappings from entities to hypernyms.
For is-a relations, we find that if y is the hypernym of x, it is likely that y is the hypernym of entities that are semantically close to x. For example,
4We do not consider the cases where a pair matches both positive and negative rules because such cases are very rare, and even non-existent in our datasets. However, our method can deal with these cases by using some simple heuristics. For example, we can update Ri using either of the following two ways: i) Ri = Fi and ii) Ri = Fi + ∑ cj∈C(xi,yi) γj .
5
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
P1 The head word of the entity x matches that of the candidate hypernym y. For example,动物 (Animal) is the correct hypernym of哺乳动物 (Mammal). N1 The head word of the entity x matches the non-head word of the candidate hypernym y. For example,动物学 (Zoology) is not a hypernym of哺乳动物 (Mammal). N2 The head word of the candidate hypernym y matches an entry in a Chinese lexicon extended based on the lexicon used in Li et al., (2015). It consists of 184 non-taxonomic, thematic words such as政治(Politics),军事(Military), etc.
Table 1: Three linguistic rules used in our work for Chinese hypernym prediction.
if we know United States is a country, we can infer country is the hypernym of similar entities such as Canada, Australia, etc. This intuition can be encoded in the similarity of the two pairs pi = (xi, yi) and pj = (xj , yj):
sim(pi, pj) =
{ cos(xi,xj) yi = yj
0 otherwise (1)
where xi is the embedding vector of xi5. This similarity indicates there exists a nonlinear mapping from entities to hypernyms, which can not be encoded in linear projection based methods (Fu et al., 2014; Wang and He, 2016). Based on TransLP (Liu and Yang, 2015), this intuition can be model as propagating class labels (is-a or not-is-a) of labeled word/phrase pairs to similar unlabeled ones based on Eq. (1). For example, the score of is-a relations between United State and country will propagate to pairs such as (Canada, country) and (Australia, country) by random walks.
Denote F∗ as the optimal solution of the problem min Os + Or. Inspired by (Liu and Yang, 2015; Xu et al., 2016), we can add a Gaussian prior N(F∗,Σ) to F where Σ is the covariance matrix and Σi,j = sim(pi, pj). Hence the optimization objective of this part is defined as: On = FTΣ−1F which is linearly proportional to the negative likelihood of the Gaussian random field prior. This means we minimize the training error and encourage F to have a smooth propagation with respect to the similarities among pairs defined by Eq. (1) at the same time.
3.3.4 Joint Optimization
Based on the three components, we minimize the following function:
J(F) = Os + Or + µ1 2 On + µ2 2 ‖F‖22 (2)
5We only consider the similarity between entities and not candidate hypernyms because the similar rule for candidate hypernyms is not true. For example, nouns close to country in our Skip-gram model are region, department, etc. They are not all correct hypernyms of United States, Canada, Australia, etc.
where ‖F‖22 imposes an additional smooth l2regularization on F. µ1 and µ2 are tuning parameters.
A basic approach to learn the optimal values of F is via gradient descent. Based on Eq. (2), the derivative of F with respect to J(F) can be computed as follows:
dJ(F) dF = W2(F−S)+(F−R)+µ1Σ−1F+µ2F
Optimizing Eq. (2) is computationally expensive when m is large. After W2, S, R and Σ−1 are pre-computed, the runtime complexity of the loop of gradient descent is O(tm2) where t is the number of iterations.
To speed up the learning process, we introduce a blockwise gradient descent technique. From the definition of Eq. (2), we can see that the optimal values of Fi and Fj with respect to (xi, yi) and (xj , yj) are irrelevant if yi 6= yj . Therefore, the original optimization problem can be decomposed and solved separately according to different candidate hypernyms.
LetH be the collection of candidate hypernyms in DU . For each h ∈ H , denote Dh as the collection of word/phase pairs in D+ ∪ D− ∪ DU that share the same candidate hypernym h. The original problem can be decomposed into |H| optimization subproblems over Dh for each h ∈ H . The runtime complexity is O( ∑ h∈Dh th|Dh|
2) where th is the number of iterations to solve the subproblem over Dh. Although we do not know the upper bounds on the numbers of iterations of these two learning techniques, the runtime complexity can be reduced by blockwise gradient descent for two reasons: i) ∑ h∈Dh |Dh| ≤ m and ii) th has a large probability to be smaller than t due to the smaller number of data instances. This technique can be also viewed as optimizing Eq. (2) based on blockwise matrix computation.
Finally, for each (xi, yi) ∈ DU , we predict that yi is a hypernym of xi if Fi > θ where θ ∈ (−1, 1) is a threshold tuned on the development set.
6
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
4 Experiments
In this section, we conduct experiments to evaluate our method. Section 4.1 to Section 4.5 report the experimental steps on Chinese datasets. We present the performance on English datasets in Section 4.6 and a discussion in Section 4.7.
4.1 Experimental Data
The Chinese text corpus is extracted from the contents of 1.2M entity pages from Baidu Baike6, a Chinese online encyclopedia. It contains approximately 1.1B words. We use the open source toolkit Ansj7 for Chinese word segmentation.
We have two collections of Chinese word/phase pairs as ground truth datasets. Each pair is labeled with an is-a or not-is-a tag. The first one (denoted as FD) is from Fu et al., (2014), containing 1,391 is-a pairs and 4,294 not-is-a pairs, which is the first publicly available dataset to evaluate this task. The second one (denoted as BK) is larger in size and crawled from Baidu Baike by ourselves, consisting of <entity, category> pairs. For each pair in BK, we ask multiple human annotators to label the tag and discard the pair with inconsistent labels by different annotators. In total, it contains 3,870 is-a pairs and 3,582 not-is-a pairs8.
In the following experiments, we use 60% of the data for training, 20% for development and 20% for testing, partitioned randomly. By rotating the 5-fold subsets of the datasets, we report the performance of each method on average.
4.2 Parameter Analysis
The word embeddings are pre-trained by ourselves on the Chinese corpus. In total, we obtain the 100- dimensional embedding vectors of 5.8M distinct words. The regularization parameters are set to λ = 10−3 and µ1 = µ2 = 10−4, fine tuned on the development set.
The choice of θ reflects the precision-recall trade-off in our model. A larger value of θ means we pay more attention to precision rather than recall. Figure 2 illustrates the precision-recall curves on both datasets. It can be seen that the performance of our method is generally better in BK than FD. The most probable cause is that BK is a large dataset with more “balanced” numbers of positive
6https://baike.baidu.com/ 7https://github.com/NLPchina/ansj seg/ 8Our dataset is publicly available. See submitted dataset.
0.0 0.2 0.4 0.6 0.8 1.0 0.0
0.2
0.4
0.6
0.8
1.0
Recall
Pr ec is io n
(a) Dataset: FD
0.0 0.2 0.4 0.6 0.8 1.0 0.0
0.2
0.4
0.6
0.8
1.0
Recall
Pr ec is io n
(b) Dataset: BK
Figure 2: Precision-recall curve with respect to the tuning of θ on development sets (%).
and negative data. Finally, θ is set to 0.05 on FD and 0.1 on BK.
4.3 Performance
In a series of previous work (Fu et al., 2013, 2014; Wang and He, 2016), several pattern-based, inference-based and encyclopedia-based is-a relation extraction methods for English have been implemented for the Chinese language. As their experiments show, these methods achieve the Fmeasure of lower than 60% in most cases, which are not suggested to be strong baselines for Chinese hypernym prediction. Interested readers may refer to their papers for the experimental results.
To make the convincing conclusion, we employ two recent state-of-the-art approaches for Chinese is-a relation identification (Fu et al., 2014; Wang and He, 2016) as baselines. We also take the word embedding based classification approach (Mirza and Tonelli, 2016)9 and Chinese Wikipedia based SVM model (Li et al., 2015) as baselines to predict is-a relations between words10. The experimental results are illustrated in Table 3.
For Fu et al., (2014), we test the performance using a linear projection model (denoted as S in Table 3) and piecewise projection models (P). It shows that the semantics of is-a relations are better modeled by multiple projection models, with a slightly improvement in F-measure. By combining iterative projection models and pattern-based validation, the most recent approach (Wang and He, 2016) increases the F-measure by 4% and 2% in two datasets. In this method, the patternbased statistics are calculated using the same cor-
9Although the experiments in their paper are mostly related to temporal relations, the method can be applied to is-a relations without modification.
10Previously, these methods used different knowledge sources to train models and thus the results in their papers are not directly comparable with ours. To make fair comparison, we take the training data as the same knowledge source to train models for all methods.
7
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
Candidate Hypernym P T Candidate Hypernym P T Entity: 乙烯(Ethylene) Entity: 孙燕姿(Stefanie Sun) 化学品(Chemical) √ √ 歌手(Singer) √ √ 有机化学(Organic Chemistry) × × 明星(Star) √ √ 有机物(Organics) √ √ 人物(Person) √ √ 气体(Gas) √ √ 金曲奖 (Golden Melody Award) √
× 自然科学(Natural Science) × × 音乐人(Musician) √ √ Entity: 显卡(Graphics Card) Entity: 核反应堆(Nuclear Reactor) 硬件(Hardware) √ √ 建筑学(Architecture) × × 电子产品(Electronic Product) √ √
核科学(Nuclear Science) × × 电脑硬件(Computer Hardware) √ √ 核能 (Nuclear Energy) √ × 数码(Digit) × × 自然科学(Natural Science) × ×
Table 2: Examples of model prediction. (P: prediction result, T: ground truth, √ : positive, ×: negative)
Dataset FD Method P R F Fu et al., (2014) (S) 64.1 56.0 59.8 Fu et al., (2014) (P) 66.4 59.3 62.6 Li et al., (2015) 54.3 38.4 45.0 Mirza and Tonelli, (2016) (C) 67.7 75.2 69.7 Mirza and Tonelli, (2016) (A) 65.3 60.7 62.9 Mirza and Tonelli, (2016) (S) 71.9 60.6 65.7 Wang and He, (2016) 69.3 64.5 66.9 Ours (Initial) 70.7 69.2 69.9 Ours 72.8 70.5 71.6 Dataset BK Method P R F Fu et al., (2014) (S) 71.4 64.8 67.9 Fu et al., (2014) (P) 72.7 67.5 70.0 Li et al., (2015) 61.2 47.5 53.5 Mirza and Tonelli, (2016) (C) 80.3 75.9 78.0 Mirza and Tonelli, (2016) (A) 72.7 65.6 68.9 Mirza and Tonelli, (2016) (S) 78.4 60.7 68.4 Wang and He, (2016) 73.9 69.8 71.8 Ours (Initial) 81.7 78.5 80.0 Ours 83.6 80.6 82.1
Table 3: Performance comparison on test sets for Chinese hypernym prediction (%).
pus over which we train word embedding models. The main reason of the improvement may be that the projection models have a better generalization power by applying an iterative learning paradigm.
Mirza and Tonelli, (2016) is implemented using three different strategies in combining the word vectors of a pair: i) concatenation xi ⊕ yi (denoted as C), ii) addition xi + yi (A) and iii) subtraction xi − yi (S). As seen, the classification models using addition and subtraction have similar performance in two datasets, while the concatenation strategy outperforms previous two approaches. Although Li et al., (2015) achieve a high performance in their dataset, this method does not perform well in ours. The most case is that the features in that work are designed specifically for the Chinese Wikipedia category system. Our initial model has a higher accuracy than all the base-
TP/TN Rate Rule P1 Rule N1 Rule N2 Dataset FD 98.6 92.3 94.1 Dataset BK 97.6 96.8 97.3
Table 4: TP/TN rates of three linguistic rules (%).
lines. By utilizing the transductive learning framework, we boost the F-measure by 1.7% and 2.1%, respectively. Therefore, our method is effective to predict hypernyms of Chinese entities.
4.4 Effectiveness of Linguistic Rules
To illustrate the effectiveness of linguistic rules, we present the true positive (or negative) rate by using one positive (or negative) rule solely, shown in Table 4. These values serve as γis in the transductive learning stage. The results indicate that these rules have high precision (over 90%) over both datasets for our task.
We state that currently we only use a few handcraft linguistic rules in our work. The proposed approach is a general framework that can encode arbitrary numbers of rules of any kind and in any language.
4.5 Error Analysis and Case Studies
We analyze correct and error cases in the experiments. Some examples of prediction results are shown in Table 2. We can see that our method is generally effective. However, some mistakes occur mostly because it is difficult to distinguish strict is-a and topic-of relations. For example, the entity Nuclear Reactor is semantically close to Nuclear Energy. The error statistics show that such kind of errors account for approximately 80.2% and 78.6% in two test sets, respectively.
Based on the literature study, we find that such problem has been also reported in (Fu et al., 2013; Wang and He, 2016). To reduce such errors, we employ the Chinese thematic lexicon based on Li
8
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
et al., (2015) in the transductive learning stage but the coverage is still limited. Two possible solutions are: i) adding more negative training data of this kind; and ii) constructing a large-scale thematic lexicon automatically from the Web.
4.6 Experiments on English Datasets
To examine how our method can benefit hypernym prediction for the English language, we use two standard datasets in this paper. The first one is a benchmark dataset for distributional semantic evaluation, i.e., BLESS (Baroni and Lenci, 2011). Because the number of pairs in BLESS is relatively small, we also use the Shwartz (Shwartz et al., 2016) dataset. In the experiments, we treat the HYPER relations as positive data (1,337 pairs) and randomly sample 30% of the RANDOM relations as negative data (3,754 pairs) in BLESS. To create a relatively balanced dataset, we take the random split of Shwartz as input and use only 30% of the negative pairs. The dataset contains 14,135 positive pairs and 16,956 negative pairs. The text corpus is English Wikipedia and the embedding vectors are set to 100 dimensions.
For comparison, we test all the baselines over English datasets except Li et al., (2015). This is because most features in Li et al., (2015) can only be used in the Chinese environment. To implement Wang and He., (2016) for English, we use the original Hearst patterns (Hearst, 1992) to perform relation selection and do not consider not-isa patterns. We also take two recent DSM based approaches (Lenci and Benotto, 2012; Santus et al., 2014) as baselines. As for our own method, we do not use linguistic rules in Table 1 for English. The results are illustrated in Table 5. As seen, our method is superior to all the baselines over BLESS, with an F-measure of 81.9%. In Shwartz, while the approach (Mirza and Tonelli, 2016) has the highest F-measure of 80.1%, our method is generally comparable to theirs and outperforms others. The results suggest that although our method is not necessarily the state-of-the-art for English hypernym prediction, it has several potential applications. Refer to Section 4.7 for discussion.
4.7 Discussion
From the experiments, we can see that the proposed approach outperforms the state-of-the-art methods for Chinese hypernym prediction. Although the English language is not our focus, our approach still has relatively high performance.
Dataset BLESS Method P R F Lenci and Benotto, (2012) 42.8 38.6 40.6 Santus et al., (2014) 59.2 52.3 55.4 Fu et al., (2014) (S) 65.3 62.4 63.8 Fu et al., (2014) (P) 68.1 64.2 66.1 Mirza and Tonelli, (2016) (C) 79.4 84.1 81.7 Mirza and Tonelli, (2016) (A) 80.7 72.3 76.3 Mirza and Tonelli, (2016) (S) 78.0 81.2 79.6 Wang and He, (2016) 76.2 75.4 75.8 Ours (Initial) 79.3 76.3 77.7 Ours 84.4 79.5 81.9 Dataset Shwartz Method P R F Lenci and Benotto, (2012) 38.5 50.1 43.5 Santus et al., (2014) 51.2 71.5 59.6 Fu et al., (2014) (S) 65.6 66.1 65.8 Fu et al., (2014) (P) 62.3 71.9 67.3 Mirza and Tonelli, (2016) (C) 79.3 80.9 80.1 Mirza and Tonelli, (2016) (A) 79.1 79.6 79.4 Mirza and Tonelli, (2016) (S) 80.5 77.5 79.0 Wang and He, (2016) 75.1 76.3 75.6 Ours (Initial) 77.2 76.8 77.0 Ours 79.1 77.5 78.3
Table 5: Performance comparison on test sets for English hypernym prediction (%).
Additionally, our work has potential values for the following applications:11
Domain-specific or Context-sparse Relation Extraction. If the task is to predict relations between words when it is related to a specific domain or the contexts are sparse, even for English, traditional pattern-based methods are likely to fail. Our method can predict the existence of relations without explicit textual patterns and requires a relatively small amount of pairs as training data.
Under-resourced Language Learning. Our method can be adapted for relation extraction in languages with flexible expressions, few knowledge resources and/or low-performance NLP tools. This is because it does not require deep NLP parsing of sentences in a text corpus.
5 Conclusion
In summary, this paper introduces a transuctive learning approach for Chinese hypernym prediction. By modeling linear projection models, linguistic rules and non-linear mappings, our method is able to identify Chinese hypernyms with high accuracy. Experiments show that the performance of our method outperforms previous approaches. We also discuss the potential applications of our method besides Chinese hypernym prediction.
11The implementations of these applications are beyond the scope of this paper.
9
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899

Review:
The paper is clearly written, and the claims are well-supported.  The Related Work in particular is very thorough, and clearly establishes where the proposed work fits in the field.  I had two main questions about the method: (1) phrases are mentioned in section 3.1, but only word representations are discussed.  How are phrase representations derived? (2) There is no explicit connection between M^+ and M^- in the model, but they are indirectly connected through the tanh scoring function.  How do the learned matrices compare to one another (e.g., is M^- like -1*M^+?)?  Furthermore, what would be the benefits drawbacks of linking the two together directly, by enforcing some measure of dissimilarity?  Additionally, statistical significance of the observed improvements would be valuable.  Typographical comments: - Line 220:  word phase pair  should be  word phrase pair  - Line 245: I propose an alternate wording: instead of  entities are translated to,  say  entities are mapped to .  At first, I read that as a translation operation in the vector space, which I think isn't exactly what's being described. - Line 587:  slightly improvement in F-measure  should be  slight improvement in F-measure  - Line 636: extraneous commas in citation - Line 646:  The most case  should be  The most likely case  (I'm guessing) - Line 727: extraneous period and comma in citation