Title: SYNONYMNET: MULTI-CONTEXT BILATERAL MATCHING FOR ENTITY SYNONYMS

Abstract: Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery. Existing works either require structured annotations, or fail to incorporate context information effectively, which lower the efficiency of information usage. In this paper, we propose a framework for synonym discovery from free-text corpus without structured annotation. As one of the key components in synonym discovery, we introduce a novel neural network model SYNONYMNET to determine whether or not two given entities are synonym with each other. Instead of using entities features, SYNONYMNET makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema to determine synonymity. Experimental results demonstrate that the proposed model achieves state-of-the-art results on both generic and domain-specific synonym datasets: Wiki+Freebase, PubMed+UMLS and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve (AUC) and 3.19% in terms of Mean Average Precision (MAP) compare to the best baseline method.

1. INTRODUCTION:

Discovering synonymous entities from a massive corpus is an indispensable task for automated knowledge discovery. For each entity, its synonyms refer to the entities that can be used interchangeably under certain contexts. For example, Clogged Nose and Nasal Congestion are synonyms relative to the context in which they are mentioned. Given two entities, the synonym discovery task determines how likely these two entities are synonym with each other. The main goal of synonym discovery is to learn a metric that distinguishes synonym entities from non-synonym ones.

The synonym discovery task is challenging to deal with, a part of which due to the various entity expressions. For example, U.S.A/ United States of America/ United States/ U.S. refer to the same entity but expressed quite differently. Recent works on synonym discovery focus on learning the similarity from entities and their character-level features (Neculoiu et al., 2016; Mueller & Thyagarajan, 2016) . These methods work well for synonyms that share a lot of character-level features like airplane/ aeroplane or an entity and its abbreviation like Acquired Immune Deficiency Syndrome/ AIDS. However, a much larger number of synonym entities in the real world do not share a lot of character-level features, such as JD/ law degree, or clogged nose/ nasal congestion. With only character-level features being used, these models hardly obtain the ability to discriminate entities that share similar semantics but are not alike verbatim.

Context information is helpful in indicating entity synonymity, as the meaning of an entity can be better reflected by the contexts in which it appears. Modeling the context for entity synonym usually suffers from following challenges: 1) Semantic Structure. Context, as a snippet of natural language sentence, is essentially semantically structured. Some existing models encode the semantic structures in the contexts implicitly during the entity representation learning (Mikolov et al., 2013; Pennington et al., 2014; Peters et al., 2018) . The context-aware entity representations embody meaningful semantics: entities with similar contexts are likely to live in proximity in the embedding space. Some other works extract and model contexts in an explicit manner with structured annotations. Structured annotations such as dependency parsing (Qu et al., 2017) , user click information (Wei et al., 2009) , or signed heterogeneous graphs (Ren & Cheng, 2015) are introduced to guide synonym discovery. 2) Diverse Contexts. An entity can be mentioned under a wide range of circumstances. Previous works on context-based synonym discovery either focus on entity information only (Neculoiu et al., 2016; Mueller & Thyagarajan, 2016) , or use a single piece of context for each entity (Liao et al., 2017; Qu et al., 2017) to learn a similarity function for entity matching. While in practice, similar context is only a sufficient but not necessary condition for context matching. Notably, in some domains such as medical, the context expression preference varies a lot from individuals. For example, sinus congestion is usually referred by medical professionals in the medical literature, while patients often use stuffy nose on social media. It is not practical to assume that each piece of context is equally informative to represent the meaning of an entity: a context may contribute differently when matched with different contexts of other entities. Thus it is imperative to focus on multiple pieces of contexts with a dynamic matching schema for accuracy and robustness.

In light of these challenges, we propose a framework to discover synonym entities from a massive corpus without additional structured annotation. Candidate entities are obtained from a massive text corpus unsupervisely. A novel neural network model SYNONYMNET is proposed to detect entity synonyms based on two given entities via a bilateral matching among multiple pieces of contexts in which each entity appears. A leaky unit is designed to explicitly alleviate the noises from uninformative context during the matching process.

The contribution of this work is summarized as follows:

• We propose SYNONYMNET, a context-aware bilateral matching model to detect entity synonyms. SYNONYMNET utilizes multiple pieces of contexts in which each entity appears, and a bilateral matching schema with leaky units to determine entity synonymity.

• We introduce a synonym discovery framework that adopts SYNONYMNET to obtain synonym entities from a free-text corpus without additional structured annotation.

• Experiments on generic and domain-specific real-world datasets in English and Chinese demonstrate the effectiveness of the proposed model for synonym discovery.

2. SYNONYMNET:

We introduce SYNONYMNET, our proposed model that detects whether or not two entities are synonyms to each other based on a bilateral matching between multiple pieces of contexts in which entities appear. Figure 1 gives an overview of the proposed model. 

Clogged Nose Nasal Congestion (+):

The predominant symptom of this condition is Nasal Congestion, which has a significant impact ……. Figure 1 : Overview of the proposed model SYNONYMNET. The diamonds are entities. Each circle is associated with a piece of context in which an entity appears. SYNONYMNET learns to minimize the loss calculated using multiple pieces of contexts via bilateral matching with leaky units.

2.1. CONTEXT RETRIEVER:

For each entity e, the context retriever randomly fetches P pieces of contexts from the corpus D in which the entity appears. We denote the retrieved contexts for e as a set C = {c 0 , c 1 , ..., c P }, where P is the number of context pieces. Each piece of context c p ∈ C contains a sequence of words c p = (w

EQUATION

p , ..., w (T ) p ), where T is the length of the context, which varies from one instance to another. w (t) p is the t-th word in the p-th context retrieved for an entity e.

2.2. CONFLUENCE CONTEXT ENCODER:

For the p-th context c p , an encoder tries to learn a continuous vector that represents the context. For example, a recurrent neural network (RNN) such as a bidirectional LSTM (Bi-LSTM) (Hochreiter & Schmidhuber, 1997 ) can be applied to sequentially encode the context into hidden states:

EQUATION

where w

(t)

p is the word embedding vector used for the word w p from the backward LSTM bw to obtain the context vector h p for c p :

EQUATION

p ]. However, such approach does not explicitly consider the location where the entity is mentioned in the context. As the context becomes longer, it is getting risky to simply rely on the gate functions of LSTM to properly encode the context.

We introduce an encoder architecture that models contexts for synonym discovery, namely the confluence context encoder. The confluence context encoder learns to encode the local information around the entity from the raw context, without utilizing additional structured annotations. It focuses on both forward and backward directions. However, the encoding process for each direction ceases immediately after it goes beyond the entity word in the context:

h p = [ → h (te) p , ← h (te) p ],

where t e is the index of the entity word e in the context and h p ∈ R 1×d CE . By doing this, the confluence context encoder summarizes the context while explicitly considers the entity's location in the context, where no additional computation cost is introduced.

Comparing with existing works for context modeling (Cambria et al., 2018) where the left context and right context are modeled separately, but with the entity word being discarded, the confluence context encoder preserves entity mention information as well as the inter-dependencies between the left and right contexts.

2.3. BILATERAL MATCHING WITH LEAKY UNIT:

Considering the base case, where we want to identify whether or not two entities, say e and k, are synonyms with each other, we propose to find the consensus information from multiple pieces of contexts via a bilateral matching schema. Recall that for entity e, P pieces of contexts H = {h 1 , h 2 , ..., h P } are randomly fetched and encoded. And for entity k, we denote Q pieces of contexts being fetched and encoded as G = {g 1 , g 2 , ..., g Q }. Instead of focusing on a single piece of context to determine entity synonymity, we adopt a bilateral matching between multiple pieces of encoded contexts for both accuracy and robustness.

H→G matching phrase: For each h p in H and g q in G, the matching score m p→q is calculated as:

m p→q = exp(hpWBMg T q ) p ∈P exp(h p WBMg T q ) , where W BM ∈ R d CE ×d CE is a bi-linear weight matrix.

Similarly, the H←G matching phrase considers how much each context g q ∈ G could be useful to

h p ∈ H: m p←q = exp(gqWBMh T p ) q ∈Q

exp(g q WBMh T p ) . Note that P × Q matching needs to be conducted in total for each entity pair. We write the equations for each h p ∈ H and g q ∈ G for clarity. Regarding the implementation, the bilateral matching can be easily written and effectively computed in a matrix form, where a matrix multiplication is used HW BM G T ∈ R P ×Q where H ∈ R P ×D CE and G ∈ R Q×D CE . The matching score matrix M can be obtained by taking softmax on the HW BM G T matrix over certain axis (over 0-axis for M p→q , 1-axis for M p←q ). Not all contexts are informative during the matching for two given entities. For example, some contexts may contain intricate contextual information even if they mention the entity explicitly. In this work, we introduce a leaky unit during the bilateral matching, so that uninformative contexts can be routed via the leaky unit rather than forced to be matched with any informative contexts. The leaky unit is a domain-dependent vector l ∈ R 1×d CE learned with the model. For simplicity, we keep l as a zero vector. If we use the H→G matching phrase as an example, the matching score from the leaky unit l to the q-th encoded context in g q is:

m l→q = exp(lW BM g T q ) exp(lW BM g T q ) + p ∈P exp(h p W BM g T q )

.

(2)

Then, if there is any uninformative context in H, say the p-th encoded context, h p will contribute less when matched with g q due to the leaky effect: when h p is less informative than the leaky unit l.

m p→q = exp(h pW BM g T q ) exp(lW BM g T q ) + p ∈P exp(h p W BM g T q )

.

(3)

2.4. CONTEXT AGGREGATION:

The informativeness of a context for an entity should not be a fixed value: it heavily depends on the other entity and the other entity's contexts that we are comparing with. The bilateral matching scores indicate the matching among multiple pieces of encoded contexts for two entities. For each piece of encoded context, say g q for the entity k, we use the highest matched score with its counterpart as the relative informativeness score of g q to k, denote as a q = max(m p→q |p ∈ P ). Then, we aggregate multiple pieces of encoded contexts for each entity to a global context based on the relative informativeness scores:

for entity e: h = p∈P a p h p , for entity k: ḡ = q∈Q a q g q .

(4)

Note that due to the leaky effect, less informative contexts are not forced to be heavily involved during the aggregation: the leaky unit may be more competitive than contexts that are less informative, thus assigned with larger matching scores. However, as the leaky unit is not used for aggregation, scores on informative contexts become more salient during context aggregation.

2.5. TRAINING OBJECTIVES:

We introduce two architectures for training the SYNONYMNET: a siamese architecture and a triplet architecture.

Siamese Architecture The Siamese architecture takes two entities e and k, along with their contexts H and G as the input. The following loss function L Siamese is used in training for the Siamese architecture:

L Siamese = yL + (e, k) + (1 -y)L -(e, k),

(5) where it contains losses for two cases: L + (e, k) when e and k are synonyms to each other (y = 1), and L -(e, k) when e and k are not (y = 0). Specifically, inspired by Neculoiu et al. (2016) , we have

EQUATION

where s(•) is a similarity function, e.g. cosine similarity, and m is the margin value. L + (e, k) decreases monotonically as the similarity score becomes higher within the range of [-1,1]. L + (e, k) = 0 when s( h, ḡ) = 1. For L -(e, k), it remains zero when s( h, ḡ) is smaller than a margin m.

Otherwise L -(e, k) increases as s( h, ḡ) becomes larger.

Triplet Architecture The Siamese loss makes the model assign rational pairs with absolute high scores and irrational ones with low scores, while the rationality of entity synonymity could be quite relative to the context. The triplet architecture learns a metric such that the global context h of an entity e is relatively closer to a global context ḡ+ of its synonym entity, say k + , than it is to the global context ḡof a negative example ḡby some margin value m. The following loss function L Triplet is used in training for the Triplet architecture:

EQUATION

2.6. INFERENCE:

The objective of the inference phase is to discover synonym entities for a given query entity from the corpus effectively. We utilize context-aware word representations to obtain candidate entities that narrow down the search space. The SYNONYMNET verifies entity synonymity by assigning a synonym score for two entities based on multiple pieces of contexts. The overall framework is described in Figure 2 .

Query When given a query entity e, it is tedious and very ineffective to verify its synonymity with all the other possible entities. In the first step, we train entity representation unsupervisely from the massive corpus D using methods such as skipgram (Mikolov et al., 2013) or GloVe (Pennington et al., 2014 ).

An embedding matrix can be learned W EMBED ∈ R v×dEMBED , where v is the number of unique tokens in D. Although these unsupervised methods utilize the context information to learn semantically meaningful representations for entities, they are not directly applicable to entity synonym discovery. However, they do serve as an effective way to obtain candidates as they tend to give entities with similar neighboring context words similar representations. For example, nba championship, chicago black hawks and american league championship series have similar representations because they tend to share some similar neighboring words. But they are not synonyms with each other.

In the second step, we construct a candidate entity list E N N by finding nearest neighbors of a query entity e in the entity embedding space of R dEMBED . Ranking entities by their proximities with the query entity on the entity embedding space significantly narrows down the search space for synonym discovery.

For each candidate entity e N N ∈ E N N and the query entity e, we randomly fetch multiple pieces of contexts in which entities are mentioned, and feed them into the proposed SYNONYMNET model.

SYNONYMNET calculates a score s(e, e N N ) based on the bilateral matching with leaky units over multiple pieces of contexts. The candidate entity e N N is considered as a synonym to the query entity e when it receives a higher score s(e, e N N ) than other non-synonym entities, or exceeds a specific threshold. In appendix A, we provide pseudo codes for the synonym discovery using SYNONYMNET.

3. EXPERIMENTS:

3.1 EXPERIMENT SETUP Datasets Three datasets are prepared to show the effectiveness of the proposed model on synonym discovery. The Wiki dataset contains 6.8M documents from Wikipedia 1 with generic synonym entities obtained from Freebase 2 . The PubMed is an English dataset where 0.82M research paper abstracts are collected from PubMed 3 and UMLS 4 contains existing entity synonym information in the medical domain. The Wiki + FreeBase and PubMed + UMLS are public available datasets used in previous synonym discovery tasks (Qu et al., 2017) . The MedBook is a Chinese dataset collected by authors where we collect 0.51M pieces of contexts from Chinese medical textbooks as well as online medical question answering forums. Synonym entities in the medical domain are obtained from MKG, a medical knowledge graph. Preprocessing Wiki +Freebase and PubMed + UMLS come with entities and synonym entity annotations, we adopt the Stanford CoreNLP package to do the tokenization. For MedBook, a Chinese word segmentation tool Jieba5 is used to segment the corpus into meaningful entities and phrases. We remove redundant contexts in the corpus and filter out entities if they appear in the corpus less than five times. For entity representations, the proposed model works with various unsupervised word embedding methods. Here for simplicity, we adopt skip-gram (Mikolov et al., 2013) with a dimension of 200. Context window is set as 5 with a negative sampling of 5 words for training.

Evaluation Metric For synonym detection using SYNONYMNET and other alternatives, we train the models with existing synonym and randomly sampled entity pairs as negative samples. During testing, we also sample random entity pairs as negative samples to evaluate the performance. Note that all test synonym entities are from unobserved groups of synonym entities: none of the test entities is observed in the training data. Thus evaluations are done in a completely cold-start setting.

The area under the curve (AUC) and Mean Average Precision (MAP) are used to evaluate the model. AUC is used to measure how well the models assign high scores to synonym entities and low scores to non-synonym entities. An AUC of 1 indicates that there is a clear boundary between scores of synonym entities and non-synonym entities. Additionally, a single-tailed t-test is conducted to evaluate the significance of performance improvements when we compare the proposed SYNONYMNET model with all the other baselines.

For synonym discovery during the inference phase, we obtain candidate entities E N N from Knearest neighbors of the query entity in the entity embedding space, and rerank them based on the output score s(e, e N N ) of the SYNONYMNET for each e N N ∈ E N N . We expect candidate entities in the top positions are more likely to be synonym with the query entity. We report the precision at position K (P@K), recall at position K (R@K), and F1 score at position K (F1@K).

Baselines We compare the proposed model with the following alternatives. (1) word2vec (Mikolov et al., 2013) : a word embedding approach based on entity representations learned from the skipgram algorithm. We use the learned word embedding to train a classifier for synonym discovery. A scoring function Score D (u, v) = x u Wx T v is used as the objective. ( 2) GloVe (Pennington et al., 2014) : another word embedding approach. The entity representations are learned based on the GloVe algorithm. The classifier is trained with the same scoring function Score D , but with the learned glove embedding for synonym discovery. (3) SRN (Neculoiu et al., 2016) : a character-level approach that uses a siamese multi-layer bi-directional recurrent neural networks to encode the entity as a sequence of characters. The hidden states are averaged to get an entity representation. Cosine similarity is used in the objective. ( 4) MaLSTM (Mueller & Thyagarajan, 2016) : another characterlevel approach. We adopt MaLSTM by feeding the character-level sequence to the model. Unlike SRN that uses Bi-LSTM, MaLSTM uses a single direction LSTM and l-1 norm is used to measure the distance between two entities. ( 5) DPE (Qu et al., 2017) : a model that utilizes dependency parsing results as the structured annotation on a single piece of context for synonym discovery. ( 6) SYNONYMNET is the proposed model, we used siamese loss (Eq. 6) and triplet loss (Eq. 7) as the objectives, respectively.

3.2. PERFORMANCE EVALUATION:

We first apply random search to obtain the best-performing hyperparameter setting on the validation set. The hyperparameter settings, as well as the sensitivity analysis, are reported in Appendix B. We report Area Under the Curve (AUC) and Mean Average Precision (MAP) on three datasets in Table 2 . From the upper part of Table 2 we can see that SYNONYMNET performances consistently better than other baselines on three datasets. SYNONYMNET with the triplet training objective achieves the best performance on Wiki +Freebase, while the Siamese objective works better on PubMed + UMLS and MedBook + MKG. Word2vec is generally performing better than GloVe. SRNs achieve decent performance on PubMed + UMLS and MedBook + MKG. This is probably because the synonym entities obtained from the medical domain tend to share more character-level similarities, such as 6-aminohexanoic acid and aminocaproic acid. However, even if the character-level features are not explicitly used in our model, our model still performances better, by exploiting multiple pieces of contexts effectively. DPE has the best performance among other baselines, by annotating each piece of context with dependency parsing results. However, the dependency parsing results could be error-prone for the synonym discovery task, especially when two entities share the similar usage but with different semantics, such as NBA finals and NFL playoffs. Additional results are reported in Appendix C.

We conduct statistical significance tests to validate the performance improvement. The single-tailed t-test is performed for all experiments, which measures whether or not the results from the proposed model are significantly better than ones from baselines. The numbers with † markers in Table 2 indicate that the improvement is significant with p<0.05.

MODEL:

Wiki + Freebase PubMed + UMLS MedBook + MKG AUC MAP AUC MAP AUC MAP word2vec (Mikolov et al., 2013) 0.9272 0.9371 0.9301 0.9422 0.9393 0.9418 GloVe (Pennington et al., 2014) 0.9188 0.9295 0.8890 0.8869 0.7250 0.7049 SRN (Neculoiu et al., 2016) 0.8864 0.9134 0.9517 0.9559 0.9419 0.9545 MaLSTM (Mueller & Thyagarajan, 2016) Besides numeric metrics, we also use box plots to represent the score distributions for each method on all three datasets in Figure 3 . The red bars indicate scores on positive entity pairs that are synonym with each other, while the blue bars indicate scores on negative entity pairs. A general conclusion is that our model assigns higher scores for synonym entity pairs, marginally higher than other nonsynonym entity pairs when compared with other alternatives. 

3.3. ABLATION STUDY:

To study the contribution of different modules of SYNONYMNET for synonym discovery, we also report ablation test results in the lower part of 

4. RELATED WORKS:

4.1 SYNONYM DISCOVERY The synonym discovery focuses on detecting entity synonyms. Most existing works try to achieve this goal by learning from structured information such as query logs (Ren & Cheng, 2015; Chaudhuri et al., 2009; Wei et al., 2009) . While in this work, we focus on synonym discovery from free-text natural language contexts, which requires less annotation and is more challenging.

Some existing works try to detect entity synonyms by entity-level similarities (Lin et al., 2003; Roller et al., 2014; Neculoiu et al., 2016; Wieting et al., 2016) . For example, Roller et al. (2014) introduce distributional features for hypernym detection. Neculoiu et al. (2016) use a Siamese structure that treats each entity as a sequence of characters, and uses a Bi-LSTM to encode the entity information. Such approach may be helpful for synonyms with similar spellings, or dealing with abbreviations. Without considering the context information, it is hard for the aforementioned methods to infer synonyms that share similar semantics but are not alike verbatim, such as JD and law degree.

Various approaches (Snow et al., 2005; Sun & Grishman, 2010; Liao et al., 2017; Cambria et al., 2018) are proposed to incorporate context information to characterize entity mentions. However, these models are not designed for synonym discovery. Qu et al. (2017) utilize additional structured annotations, e.g. dependency parsing result, as the context of the entity for synonym discovery.

While we aim to discover synonym entities from a free-text corpus without structured annotation.

4.2. SENTENCE MATCHING:

There is another related research area that studies sentence matching. Early works try to learn a meaningful single vector to represent the sentence (Tan et al., 2015; Mueller & Thyagarajan, 2016) . These models do not consider the word-level interactions from two sentences during the matching. Wang & Jiang (2016) ; Wang et al. (2016; 2017) introduce multiple instances for matching with varying granularities. Although the above methods achieve decent performance on sentence-level matching, the sentence matching task is different from context modeling for synonym discovery in essence. Context matching focuses on local information, especially the words before and after the entity word; while the overall sentence could contain much more information, which is useful to represent the sentence-level semantics, but can be quite noisy for context modeling. We adopt a confluence encoder to model the context, which is able to aware of the location of an entity in the context while preserving information flow from both left and right contexts.

Moreover, sentence matching models do not explicitly deal with uninformative instances: maxpooling strategy and attention mechanism are introduced. The max-pooling strategy picks the most informative one and ignores all the other less informative ones. In context matching, such property could be unsatisfactory as an entity is usually associated with multiple contexts. We adopt a bilateral matching which involves a leaky unit to explicitly deal with uninformative contexts, so as to eliminate noisy contexts while preserving the expression diversity from multiple pieces of contexts.

5. CONCLUSIONS:

In this paper, we propose a framework for synonym discovery from free-text corpus without structured annotation. A novel neural network model SYNONYMNET is introduced for synonym detection, which tries to determine whether or not two given entities are synonym with each other. SYNONYMNET makes use of multiple pieces of contexts in which each entity is mentioned, and compares the context-level similarity via a bilateral matching schema to determine synonymity. Experiments on three real-world datasets show that the proposed method SYNONYMNET has the ability to discover synonym entities effectively on both generic datasets (Wiki+Freebase in English), as well as domain-specific datasets (PubMed+UMLS in English and MedBook+MKG in Chinese) with an improvement up to 4.16% in AUC and 3.19% in MAP.

The model achieves the best AUC and MAP when the maximum context length T = 50: longer contexts may introduce too much noise while shorter contexts may be less informative. A margin m of 0.75 is used for training with triplet objective. 

C CASE STUDIES:

We provide additional results and case studies in this section. Table 5 reports the performance in P@K, R@K, and F1@K. Table 6 shows a case for entity UNGA. The candidates are generated with pretrained word embedding using skip-gram.

Wiki + Freebase PubMed + UMLS MedBook + MedKG P@K R@K F1@K P@K R@K F1@K P@K R@K F1@K K=1 0.3455 0.3455 0.3455 0.2400 0.0867 0.1253 0.3051 0.2294 0.2486 K=5 0.1818 0.9091 0.3030 0.2880 0.7967 0.3949 0.2388 0.8735 0.3536 K=10 0.1000 1.0000 0.1818 0.1800 1.0000 0.2915 0.1418 1.0000 0.2360 

:

https://www.wikipedia.org/

https://developers.google.com/freebase

https://www.ncbi.nlm.nih.gov/pubmed

https://www.nlm.nih.gov/research/umls/

https://github.com/fxsjy/jieba

Figures and Tables:

in the forward LSTM f w with the first hidden state

Figure 2: Synonym discovery during the inference phase with SYNONYMNET. (1): Obtain entity representations W EMBED from the corpus D. (2): For each query entity e, search in the entity embedding space and construct a candidate entity set E N N . (3): Retrieve contexts for the query entity e and each candidate entity e N N ∈ E N N from the corpus D, and feed the encoded contexts into SYNONYMNET. (4): Discover synonym entities of the given entity by the output of SYNONYMNET.

Figure 3: Test synonym score distributions on positive and negative entity pairs.

Figure 4: Sensitivity analysis.

Table 1 shows the dataset statistics.

Table content: <table><tr><td>Dataset</td><td colspan="3">Wiki + FreeBase PubMed + UMLS MedBooK + MKG</td></tr><tr><td>#ENTITY</td><td>9274</td><td>6339</td><td>32,002</td></tr><tr><td>#VALID</td><td>394</td><td>386</td><td>661</td></tr><tr><td>#TEST</td><td>104</td><td>163</td><td>468</td></tr><tr><td>#SYNSET</td><td>4615</td><td>708</td><td>6600</td></tr><tr><td colspan="2">#CONTEXT 6,839,331</td><td>815,644</td><td>514,226</td></tr><tr><td>#VOCAB</td><td>472,834</td><td>1,069,061</td><td>270,027</td></tr><tr><td/><td/><td>Table 1: Dataset Statistics.</td><td/></tr></table>

Test performance in AUC and MAP on three datasets. † indicates the significant improvement over all baselines (p < 0.05).

Table content: <table><tr><td/><td>0.9178</td><td>0.9413</td><td>0.8151</td><td>0.8554</td><td>0.8532 0.8833</td></tr><tr><td>DPE (Qu et al., 2017)</td><td>0.9461</td><td>0.9573</td><td>0.9513</td><td>0.9623</td><td>0.9479 0.9559</td></tr><tr><td>SYNONYMNET (Pairwise)</td><td colspan="5">0.9831  † 0.9818  † 0.9838  † 0.9872  † 0.9685 0.9673</td></tr><tr><td>w/o Leaky Unit</td><td colspan="5">0.9827  † 0.9817  † 0.9815  † 0.9847  † 0.9667 0.9651</td></tr><tr><td>w/o Confluence Encoder (Bi-LSTM)</td><td colspan="3">0.9683  † 0.9625  † 0.9495</td><td>0.9456</td><td>0.9311 0.9156</td></tr><tr><td>SYNONYMNET (Triplet)</td><td colspan="5">0.9877  † 0.9892  † 0.9788  † 0.9800  † 0.9410 0.9230</td></tr><tr><td>w/o Leaky Unit</td><td colspan="5">0.9705  † 0.9631  † 0.9779  † 0.9821  † 0.9359 0.9214</td></tr><tr><td>w/o Confluence Encoder (Bi-LSTM)</td><td colspan="3">0.9582  † 0.9531  † 0.9412</td><td>0.9288</td><td>0.9047 0.8867</td></tr></table>

Table2. "w/o Confluence Context Encoder" uses the Bi-LSTM as the context encoder. The last hidden states in both forward and backward directions in Bi-LSTM are concatenated; "w/o Leaky Unit" does not have the ability to ignore uninformative contexts during the bilateral matching process: all contexts retrieved based on the entity, whether informative or not, are utilized in bilateral matching. From the lower part of Table2we can see that both modules (Leaky Unit and Confluence Encoder) contribute to the effectiveness of the model. The leaky unit contributes 1.72% improvement in AUC and 2.61% improvement in MAP on the Wiki dataset when trained with the triplet objective. The Confluence Encoder gives the model an average of 3.17% improvement in AUC on all three datasets, and up to 5.17% improvement in MAP.

Table content: <table/>

Performance on Synonym Discovery.

Table content: <table><tr><td>Candidate Entities</td><td colspan="2">Cosine Similarity Final Entities</td><td>SYNONYMNET Score</td></tr><tr><td colspan="2">united nations general assembly||m.07vp7|| 0.847374</td><td colspan="2">united nations general assembly||m.07vp7|| 0.842602</td></tr><tr><td>un human rights council</td><td>0.823727</td><td>the united nations general assembly</td><td>0.801745</td></tr><tr><td>the united nations general assembly</td><td>0.813736</td><td>unga||m.07vp7||</td><td>0.800719</td></tr><tr><td>un security council||m.07vnr||</td><td>0.794973</td><td/><td/></tr><tr><td>palestine national council</td><td>0.791135</td><td/><td/></tr><tr><td>world health assembly||m.05 gl9||</td><td>0.790837</td><td/><td/></tr><tr><td>united nations security council||m.07vnr||</td><td>0.787999</td><td/><td/></tr><tr><td>general assembly resolution</td><td>0.784581</td><td/><td/></tr><tr><td>the un security council</td><td>0.784280</td><td/><td/></tr><tr><td>ctbt</td><td>0.777627</td><td/><td/></tr><tr><td>north atlantic council||m.05pmgy||</td><td>0.775703</td><td/><td/></tr><tr><td>resolution 1441</td><td>0.773064</td><td/><td/></tr><tr><td>non-binding resolution||m.02pj22f||</td><td>0.771475</td><td/><td/></tr><tr><td>unga||m.07vp7||</td><td>0.770623</td><td/><td/></tr></table>

Discovered Synonym Entities for UNGA using SYNONYMNET. A threshold of 0.8 on the SYNONYMNET score is used.

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Peer review (iclr2019):
 This paper studies the problem of identifying (discovering) synonymous entities. The paper proposes using the "contexts" of the entities as they occur in associated text corpora (e.g. Wiki) in the proposed neural-network based embedding approach for this task. The key novelties of the approach lie in the "matching" system used, where contexts of one entity are matched with that for the other entity to see how well they align with each other (which effectively determines the similarity of the two entities). Experiments are conducted on three different datasets to show the efficacy of the proposed approach. Overall I found the paper to be an interesting read with some nice ideas mixed in. However I also had some concerns which are highlighted later down below, which I believe if addressed would lead to a very strong work. Quality: Above average In general the method seems to work somewhat better than the baselines and the method does have a couple of interesting ideas. Clarity: Average I found a few key details to be missing and also felt the paper could have been better written. Originality: Average The matching approach and use of the leaky units was interesting tidbits. Outside of that the work is largely about the application of such Siamese RNNs based networks to this specific problem. (The use of context of entities has already been looked at in previous works albeit in a slightly more limited manner) Significance: Slightly below average I am not entirely sold on the use of this approach for this problem given its complexity and unclear empirical gains vs more sophisticated baselines. The matching aspect may have some use in other problems but nothing immediately jumps out as an obvious application. ---- Strengths / Things I liked about the paper: - In general the method is fairly intuitive and simple to follow which I liked. - The matching approach was an interesting touch. - Similarly for the "leaky" unit. - Experiments conducted on multiple datasets. - The results indicate improvements over the baselines considered on all the three datasets. Weaknesses / Things that concerned me: - (W1) Slightly unfair baselines? One of the first things that struck me in the experimental results was how competitive word2vec by itself was across all three datasets. This made me wonder what would happen if we were to use a more powerful embedding approach say FastText, Elmo, Cove or the recently proposed BERT? (The proposed method itself uses bidirectional LSTMs) Furthermore all of them are equally capable of capturing the contexts as well. An even more competitive (and fair) set of baselines could have taken the contexts as well and use their embeddings as well. Currently the word2vec baseline is only using the embedding of the entity (text), whereas the proposed approach is also provided the different contexts at inference time. The paper says using the semantic structure and the diverse contexts are weaknesses of approaches using the contexts, but I don*t see any method that uses the context in an embedding manner -- say the Cove context vectors. If the claim is that they won*t add any additional value above what is already captured by the entity it would be good to empirically demonstrate this. - (W2) Significance testing: On the topic of experimentation, I was concerned that significance testing / error estimates weren*t provided for the main emprical results. The performance gaps seem to be quite small and to me it is unclear how significant these gaps are. Given how important significance testing is as an empirical practice this seems like a notable oversight which I would urge the authors to address. - (W3) Missing key details: There were some key aspects of the work that I thought were not detailed. Chief among these was the selection of the contexts for the entities. How was this? How were the 20 contexts identified? Some of these entities are likely far more common than just 20 sentences and hence I wonder how these were selected? Another key aspect I did not see addressed: How were the entities identified in the text (to be able to find the contexts for them)? The paper claims that they would like to learn from minimal human annotations but I don*t understand how these entity annotations in the text were obtained. This again seems like a notable oversight. - (W4) Concerns about the method: I had two major concerns about the method: (a) Complexity of method : I don*t see an analysis of the computational cost of the proposed method (which scales quadratically with P the number of contexts); (b) Effect of redundant "informative" contexts: Imagine you have a number of highly informative contexts for an entity but they are all very similar to each other. Due to the way the matching scores are aggregated, these scores are made to sum to 1 and hence no individual score would be very high. Given that this is the final coefficient for the associated context, this seems like a significant issue right? Unless the contexts are selected to be maximally diverse, it seems like this can essentially end up hurting an entity which occurs in similar contexts repeatedly. I would like to see have seen the rationale for this better explained. (c) A smaller concern was understanding the reasoning behind the different loss functions in the siamese loss function with a different loss for the positive and the negative, one using a margin and one which doesn*t. One which scales to 1/4, the other scaling to (1-m)^2. This seems pretty arbitrary and I*d like to understand this. -(W5) Eval setting : My last concern was with the overall evaluation setup. Knowledge bases like Freebase are optimized for precision rather than recall, which is why "discovery" of new relations is important. However if you treat all missing relationships as negative examples then how exactly are you measuring the true ability of a method? Thus overall I*m pretty skeptical about all the given numbers simply because we know the KBs are incomplete, but are penalizing methods that may potentially discover relations not in the KB.