SynSetMineRe

Reimplemtation of SynSetMine proposed in Mining Entity Synonyms with Efficient Neural Set Generation

DataSet Description

Training Data Raw Word Set

	NYT	Wiki	PubMed
Vocab Size	2573	8613	72623
Sets Nums	1273	4359	28600
Max Set sSize	5.0	3.0	19.0
Min Set Size	1.0	1.0	1.0
Average Set Size	2.03	2.00	2.53

Distribution of Dataset

Note: split 1/5 train-cold.set as dev.set

counts is the wordset size

NYT

Counts	1	2	3	4	5	6
train_set	18	1213	35	6	1	0
test_set	0	1	89	18	7	2
dev_set	0	227	20	6	1	0

PubMed

Counts	1	2	3	4	5	6	>6
train_set	0	19135	5688	187	954	2437	199
test_set	0	0	0	0	0	128	122
dev_set	0	2009	1556	1163	655	154	183

Wiki

Counts	1	2	3	4	5	6	>6
train_set	100	4243	12	6	1	0	0
test_set	0	0	176	53	16	8	3
dev_set	4	8555	12	6	1	0	0

Experiment Result

（I use 20 negative sample ratio to create negative samples）

Orignal Experiment Result in Paper

	NYT_Test	PubMed_Test	Wiki_Test
ARI	0.4491 (+0.0216)	0.7433 (+0.0066)	0.5643 (+0.0131)
NMI	0.9062 (+0.0153)	0.9490 (+0.0097)	0.9304 (+0.0023)
FMI	0.4637(+0.0192)	0.7445 (+0.0064)	0.5710 (+0.0117)

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split train-cold.set file into 1:4: dev_data and train_data

Classifier Result in NYT DataSet

	NYT_Dev	NYT_Test	PubMed_Dev	PubMed_Test	Wiki_Dev	Wiki_Test
True Positive	152	78	4306	228	638	206
True Negative	2493	1147	56702	2481	8616	2532
False Positive	47	23	498	19	94	28
False Negative	102	39	1414	22	233	50
Sum	2794	1288	62920	2750	9581	2816

	NYT_Dev	NYT_Test	PubMed_Dev	PubMed_Test	Wiki_Dev	Wiki_Test
Accuracy	0.95	0.95	0.97	0.99	0.97	0.97
Precision	0.76	0.77	0.90	0.92	0.85	0.88
Recall	0.60	0.67	0.75	0.91	0.77	0.80
F1-Score	0.67	0.72	0.82	0.92	0.81	0.84

clustering result

	NYT_Train	NYT_Test	PubMed_Train	PubMed_Test	Wiki_Train	Wiki_Test
ARI	0.16	0.33	------	0.69	0.06	0.40
NMI	0.87	0.85	------	0.93	0.83	0.88
FMI	0.23	0.34	------	0.70	0.12	0.43

(PubMed_Train is too big to predict, So I did not do this experiment)

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split train-cold.set file into 1:4: dev_data is 1/5 of train-cold.set and train_data is train-cold.set

Classifier Result in NYT DataSet

	NYT_Dev	NYT_Test	PubMed_Dev	PubMed_Test	Wiki_Dev	Wiki_Test
True Positive	1237	55	5128	230	867	190
True Negative	25455	1154	56721	2491	8624	2541
False Positive	5	16	479	9	86	19
False Negative	36	62	592	20	4	66
Sum	2794	1288	62920	2750	9581	2816

	NYT_Dev	NYT_Test	PubMed_Dev	PubMed_Test	Wiki_Dev	Wiki_Test
Accuracy	0.98	0.94	0.98	0.99	0.99	0.97
Precision	0.85	0.77	0.91	0.96	0.91	0.91
Recall	0.92	0.47	0.90	0.92	1.00	0.74
F1-Score	0.89	0.59	0.91	0.94	0.95	0.82

clustering result

	NYT_Train	NYT_Test	PubMed_Train	PubMed_Test	Wiki_Train	Wiki_Test
ARI	-----	0.42	------	0.71	------	0.45
NMI	-----	0.89	------	0.93	------	0.90
FMI	-----	0.42	------	0.71	------	0.46

(PubMed_Train is too big to predict, So I did not do this experiment)

Experiment Detail

• I freeze the embedding layer. Only use raw word embedding to train.

• Only Use Combined.embed word vector to do experiment. Other Experiment is suspended. QAQ

Result

In PubMed DataSet, I reimplement the best performance. Althought, there is a distinguish between it and result in raw paper. Compared with other method's result showed in paper, Reimplemented SysSetMine still performed better.

Extention and Explore

I try to replace the scorer using BertModel. Omit the position_ids in bert input, using a special token to split words in one word set.

Embedding Problem

Many Words is [UNK] word in Bert Pretrained Model. I try to use a classifier to map word embedding layer to bert embedding size