Venue: ACL 2017

# Improved Word Representation Learning with Sememes

Yilin Niu\({}^{1*}\), Ruobing Xie\({}^{1}\); Zhiyuan Liu\({}^{1,2}\) , Maosong Sun\({}^{1,2}\)

\({}^{1}\) Department of Computer Science and Technology,

State Key Lab on Intelligent Technology and Systems,

National Lab for Information Science and Technology, Tsinghua University, Beijing, China

\({}^{2}\) Jiangsu Collaborative Innovation Center for Language Ability,

Jiangsu Normal University, Xuzhou 221009 China

indicates equal contributionCorresponding author: Z. Liu (liuzy@tsinghua.edu.cn)

###### Abstract

Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words-s, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information.

+
Footnote †: Corresponding author: Z. Liu (liuzy@tsinghua.edu.cn)

_Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics_, pages 2049–2058 Vancouver, Canada, July 30 - August 4, 2017. ©2017 Association for Computational Linguistics [https://doi.org/10.18653/v1/P17-1187](https://doi.org/10.18653/v1/P17-1187)

## 1 Introduction

Sememes are defined as minimum semantic u-units of word meanings, and there exists a limited close set of sememes to compose the semantic meanings of an open set of concepts (i.e. word sense). However, sememes are not explicit for each word. Hence, people manually annotate word sememes and build linguistic common-sense knowledge bases.

HowNet Dong and Dong (2003) is one of such knowledge bases, which annotates each concept in Chinese with one or more relevant sememes. Different from WordNet Miller (1995), the philosophy of HowNet emphasizes the significance of part and attribute represented by sememes. HowNet has been widely utilized in word similarity computation Liu and Li (2002) and sentiment analysis Xianghua et al. (2013), and in section 3.2 we will give a detailed introduction to sememes, senses and words in HowNet.

In this paper, we aim to incorporate word sememes into word representation learning (WRL) and learn improved word embeddings in a low-dimensional semantic space. WRL is a fundamental and critical step in many NLP tasks such as language modeling Bengio et al. (2003) and neural machine translation Sutskever et al. (2014).

There have been a lot of researches for learning word representations, among which word2vec Mikolov et al. (2013) achieves a nice balance between effectiveness and efficiency. In word2vec, each word corresponds to one single embedding, ignoring the polysemy of most words. To address this issue, Huang et al. (2012) introduces a multi-prototype model for WRL, conducting unsupervised word sense induction and embeddings according to context clusters. Chen et al. (2014) further utilizes the synset information in WordNet to instruct word sense representation learning.

From these previous studies, we conclude that word sense disambiguation are critical for WR-L, and we believe that the sememe annotation of word senses in HowNet can provide necessary semantic regularization for the both tasks. To explore its feasibility, we propose a novel **S**ememe-**E**ncoded **W**ord **R**epresentation **I**earning

(SE-WRL) model, which detects word senses and learns representations simultaneously. More specifically, this framework regards each word sense as a combination of its sememes, and iteratively performs word sense disambiguation according to their contexts and learn representations of semenes, senses and words by extending Skip-gram in word2vec [14]. In this framework, an attention-based method is proposed to select appropriate word senses according to contexts automatically. To take full advantages of sememes, we propose three different learning and attention strategies for SE-WRL.

In experiments, we evaluate our framework on two tasks including word similarity and word analogy, and further conduct case studies on sememe, sense and word representations. The evaluation results show that our models outperform other baselines significantly, especially on word analogy. This indicates that our models can build better knowledge representations with the help of sememe information, and also implies the potential of our models on word sense disambiguation.

The key contributions of this work are concluded as follows: (1) To the best of our knowledge, this is the first work to utilize sememes in HowNet to improve word representation learning. (2) We successfully apply the attention scheme to detect word senses and learn representations according to contexts with the favor of the sememe annotation in HowNet. (3) We conduct extensive experiments and verify the effectiveness of incorporating word sememes for improved WRL.

## 2 Related Work

### Word Representation

Recent years have witnessed the great thrive in word representation learning. It is simple and s-traightforward to represent words using one-hot representations, but it usually struggles with the data sparsity issue and the neglect of semantic relations between words.

To address these issues, [50] proposes the idea of distributed representation which projects all words into a continuous low-dimensional semantic space, considering each word as a vector. Distributed word representations are powerful and have been widely utilized in many NLP tasks, including neural language models [6, 7], machine translation [8, 9], parsing [2] and text classification [20]. Word distributed representations are capable of encoding semantic meanings in vector space, serving as the fundamental and essential inputs of many NLP tasks.

There are large amounts of efforts devoted to learning better word representations. As the exponential growth of text corpora, model efficiency becomes an important issue. [14] proposes two models, CBOW and Skipgram, achieving a good balance between effectiveness and efficiency. These models assume that the meanings of words can be well reflected by their contexts, and learn word representations by maximizing the predictive probabilities between words and their contexts. [10] further utilizes matrix factorization on word affinity matrix to learn word representations. However, these models merely arrange only one vector for each word, regardless of the fact that many word-s have multiple senses. [11, 12] utilize multi-prototype vector models to learn word representations and build distinct vectors for each word sense. [13] presents an extension to Skip-gram model for learning non-parametric multiple embeddings per word. [12] also utilizes an Autoencoder to jointly learn word, sense and synset representations in the same semantic space.

This paper, for the first time, jointly learns representations of sememes, senses and words. The sememe annotation in HowNet provides useful semantic regularization for WRL. Moreover, the u-nified representations incorporated with sememes also provide us more explicit explanations of both word and sense embeddings.

### Word Sense Disambiguation and Representation Learning

Word sense disambiguation (WSD) aims to identify word senses or meanings in a certain context computationally. There are mainly two approaches for WSD, namely the supervised methods and the knowledge-based methods. Supervised methods usually take the surrounding words or senses as features and use classifiers like SVM for word sense disambiguation [21], which are intensively limited to the time-consuming human annotation of training data.

On contrary, knowledge-based methods utilize 

large external knowledge resources such as knowledge bases or dictionaries to suggest possible senses for a word. Banerjee and Pedersen (2002) exploits the rich hierarchy of semantic relations in WordNet Miller (1995) for an adapted dictionary-based WSD algorithm. Bordes et al. (2011) introduces synset information in WordNet to WRL Chen et al. (2014) considers synsets in WordNet as different word senses, and jointly conducts word sense disambiguation and word / sense representation learning. Guo et al. (2014) considers bilingual datasets to learn sense-specific word representations. Moreover, Jauhar et al. (2015) proposes two approaches to learn sense-specific word representations that are grounded to ontologies. Pilehvar and Collier (2016) utilizes personalized PageRank to learn de-conflated semantic representations of words.

In this paper, we follow the knowledge-based approach and automatically detect word senses according to the contexts with the favor of sememe information in HowNet. To the best of our knowledge, this is the first attempt to apply attention-based models to encode sememe information for word representation learning.

## 3 Methodology

In this section, we present our framework Sememe-Encoded WRL (SE-WRL) that considers sememe information for word sense disambiguation and representation learning. Specifically, we learn our models on a large-scale text corpus with the semantic regularization of the sememe annotation in HowNet and obtain sememe, sense and word embeddings for evaluation tasks.

In the following sections, we first introduce HowNet and the structures of sememes, senses and words. Then we discuss the conventional WRL model Skip-gram that we utilize for the sememencoded framework. Finally, we propose three sememe-encoded models in details.

### Sememes, Senses and Words in HowNet

In this section, we first introduce the arrangement of sememes, senses and words in HowNet. HowNet annotates precise senses to each word, and for each sense, HowNet annotates the significance of parts and attributes represented bySememes.

Fig. 1 gives an example ofsememes, senses and words in HowNet. The first layer represents the**word**"apple". The word "apple" actually has two main **senses** shown on the second layer: one is a sort of juicy fruit (_apple_), and another is a famous computer brand (_Apple brand_). The third and following layers are those **senemes** explaining each sense. For instance, the first sense _Apple brand_ indicates a computer brand, and thus hassememes \({\tt computer},{\tt bring}{\tt and}{\tt SpeBrand}\).

From Fig. 1 we can find that, sememes of many senses in HowNet are annotated with various relations, such as _define_ and _modifier_, and form complicated hierarchical structures. In this paper, for simplicity, we only consider all annotated sememes of each sense as a sememe set without considering their internal structure. HowNet assumes the limited annotated sememes can well represent senses and words in the real-world scenario, and thus sememes are expected to be useful for both WSD and WRL.

We introduce the notions utilized in the following sections as follows. We define the overall sememe, sense and word sets used in training as \(X\), \(S\) and \(W\) respectively. For each \(w\in W\), there are possible multiple senses \(s^{(w)}_{i}\in S^{(w)}\) where \(S^{(w)}\) represents the sense set of \(w\). Each sense \(s^{(w)}_{i}\) consists of several sememes \(x^{(s_{i})}_{j}\in X^{(w)}_{i}\). For each target word \(w\) in a sequential plain text, \(C(w)\) represents its context word set.

### Conventional Skip-gram Model

We directly utilize the widely-used model Skip-gram to implement our SE-WRL model, because Skip-gram has well balanced effectiveness as well as efficiency Mikolov et al. (2013). The standard skip-gram model assumes that word embeddings should relate to their context words. It aims at

Figure 1: Examples of sememes, senses and words.

 

maximizing the predictive probability of context words conditioned on the target word \(w\). Formally, we utilize a sliding window to select the context word set \(C(w)\). For a word sequence \(H=\{w_{1},\cdots,w_{n}\}\), Skip-gram model intends to maximize:

\[L(H)=\sum_{i=K}^{n-K}\log\Pr(w_{i-K},\cdots,w_{i+K}|w_{i}), \tag{1}\]

where \(K\) is the size of sliding window. \(\Pr(w_{i-K},\cdots,w_{i+K}|w_{i})\) represents the predictive probability of context words conditioned on the target word \(w_{i}\), formalized by the following softmax function:

\[\begin{split}&\Pr(w_{i-K},\cdots,w_{i+K}|w_{i})=\prod_{w_{c}\in C (w_{i})}\Pr(w_{c}|w_{i})\\ &\qquad\quad=\prod_{w_{c}\in C(w_{i})}\frac{\exp(\mathbf{w}_{c}^ {\top}\cdot\mathbf{w}_{i})}{\sum_{u^{\prime}_{i}\in W}\exp(\mathbf{w}_{c}^{ \top}\cdot\mathbf{w}_{i}^{\prime})},\end{split} \tag{2}\]

in which \(\mathbf{w}_{c}\) and \(\mathbf{w}_{i}\) stand for embeddings of context word \(w_{c}\in C(w_{i})\) and target word \(w_{i}\) respectively. We can also follow the strategies of hierarchical softmax and negative sampling proposed in Mikolov et al. (2013) to accelerate the calculation of softmax.

### SE-WRL Model

In this section, we introduce the SE-WRL models with three different strategies to utilize sememe information, including Simple Sememe Aggregation Model (SSA), Sememe Attention over Context Model (SAC) and Sememe Attention over Target Model (SAT).

#### 3.3.1 Simple Sememe Aggregation Model

The Simple Sememe Aggregation Model (SSA) is a straightforward idea based on Skip-gram model. For each word, SSA considers all sememes in all senses of the word together, and represents the target word using the average of all its sememe embeddings. Formally, we have:

\[\mathbf{w}=\frac{1}{m}\sum_{s_{i}^{(w)}\in S^{(w)}}\sum_{x_{j}^{(s_{i})}\in X_ {i}^{(w)}}\mathbf{x}_{j}^{(s_{i})}, \tag{3}\]

which means the word embedding of \(w\) is composed by the average of all its sememe embeddings. Here, \(m\) stands for the overall number of sememes belonging to \(w\).

This model simply follows the assumption that, the semantic meaning of a word is composed of the semantic units, i.e., sememes. As compared to the conventional Skip-gram model, since sememes are shared by multiple words, this model can utilize sememe information to encode latent semantic correlations between words. In this case, similar words that share the same sememes may finally obtain similar representations.

#### 3.3.2 Sememe Attention over Context Model

The SSA Model replaces the target word embedding with the aggregated sememe embeddings to encode sememe information into word representation learning. However, each word in SSA model still has only one single representation in different contexts, which cannot deal with polysemy of most words. It is intuitive that we should construct distinct embeddings for a target word according to specific contexts, with the favor of word sense annotation in HowNet.

To address this issue, we come up with the Sememe Attention over Context Model (SAC). SAC utilizes the attention scheme to automatically select appropriate senses for context words according to the target word. That is, SAC conducts word sense disambiguation for context words to learn better representations of target words. The structure of the SAC model is shown in Fig. 2.

More specifically, we utilize the original word embedding for target word \(w\), but use sememe embeddings to represent context word \(w_{c}\) instead of original context word embeddings. Suppose a word typically demonstrates some specific senses in one sentence. Here we employ the target word embedding as an attention to select the most appropriate senses to make up context word embeddings. We formalize th

Figure 2: Sememe Attention over Context Model.

 

\({\bf w}_{c}\) as follows:

\[{\bf w}_{c}=\sum^{|S^{(w_{c})}|}_{j=1}att(s^{(w_{c})}_{j})\cdot{\bf s }^{(w_{c})}_{j}, \tag{4}\]

where \({\bf s}^{(w_{c})}_{j}\) stands for the \(j\)-th sense embedding of \(w_{c}\), and \(att(s^{(w_{c})}_{j})\) represents the attention score of the \(j\)-th sense with respect to the target word \(w\), defined as follows:

\[att(s^{(w_{c})}_{j})=\frac{\exp({\bf w}\cdot\hat{\mathbf{s}}^{(w_{ c})}_{j})}{\sum^{|S^{(w_{c})}_{k=1}|}_{k=1}\exp({\bf w}\cdot\hat{\mathbf{s}}^{(w _{c})}_{k})}. \tag{5}\]

Note that, when calculating attention, we use the average of sememe embeddings to represent each sense \(s^{(w_{c})}_{j}\):

\[\hat{\mathbf{s}}^{(w_{c})}_{j}=\frac{1}{|X^{(w_{c})}_{j}|}\sum^{| X^{(w_{c})}_{j}|}_{k=1}{\bf x}^{(s_{j})}_{k}. \tag{6}\]

The attention strategy assumes that the more relevant a context word sense embedding is to the target word \({\bf w}\), the more this sense should be considered when building context word embeddings. With the favor of attention scheme, we can represent each context word as a particular distribution over its sense. This can be regarded as soft WSD. As shown in experiments, it will help learn better word representations.

#### 3.3.3 Sememe Attention over Target Model

The Sememe Attention over Context Model can flexibly select appropriate senses and sememes for context words according to the target word. The process can also be applied to select appropriate senses for the target word, by taking context words as attention. Hence, we propose the Sememe Attention over Target Model (SAT) as shown in Fig. 3.

Different from SAC model, SAT learns the o-riginal word embeddings for context words, but sememe embeddings for target words. We apply context words as attention over multiple senses of the target word \(w\) to build the embedding of \(w\), formalized as follows:

\[{\bf w}=\sum^{|S^{(w)}|}_{j=1}att(s^{(w)}_{j})\cdot{\bf s}^{(w)}_{ j}, \tag{7}\]

where \({\bf s}^{(w)}_{j}\) stands for the \(j\)-th sense embedding of \(w\), and the context-based attention is defined as follows:

\[att(s^{(w)}_{j})=\frac{\exp({\bf w}^{\prime}_{c}\cdot\hat{\mathbf{s}}^{(w)}_{ j})}{\sum^{|S^{(w)}|}_{k=1}\exp({\bf w}^{\prime}_{c}\cdot\hat{\mathbf{s}}^{(w)}_{ k})}, \tag{8}\]

where, similar to Eq. (6), we also use the average of sememe embeddings to represent each sense \(s^{(w)}_{j}\). Here, \({\bf w}^{\prime}_{c}\) is the context embedding, consisting of a constrained window of word embeddings in \(C(w_{i})\). We have:

\[{\bf w}^{\prime}_{c}=\frac{1}{2K^{\prime}}\sum^{k=i+K^{\prime}}_{k=i-K^{ \prime}}{\bf w}_{k},~{}~{}~{}k\neq i. \tag{9}\]

Note that, since in experiment we find the sense s-election of the target word only relies on more limited context words for calculating attention, hence we select a smaller \(K^{\prime}\) as compared to \(K\).

Recall that, SAC only uses one target word as attention to select senses of context words, but SAT uses several context words together as attention to select appropriate senses of target words. Hence SAT is expected to conduct more reliable WSD and result in more accurate word representations, which will be explored in experiments.

## 4 Experiments

In this section, we evaluate the effectiveness of our SE-WRL1 models on two tasks including word similarity and word analogy, which are two classical evaluation tasks mainly focusing on evaluating the quality of learned word representations. We also explore the potential of our models in word sense disambiguation with case study, showing the power of our attention-based models.

Figure 3: Sememe Attention over Target Model.

 



### Dataset

We use the web pages in Sogou-T\({}^{2}\) as the text corpus to learn WRL models. Sogou-T is provided by a Chinese commercial search engine, which contains \(2.7\) billion words in total.

We also utilize the sememe annotation in HowNet. The number of distinct sememes used in this paper is \(1,889\). The average senses for each word are about \(2.4\), while the average sememes for each sense are about \(1.6\). Throughout the Sogou-T corpus, we find that \(42.2\%\) of words have multiple senses. This indicates the significance of WSD.

For evaluation, we choose wordsim-240 and wordsim-2973 to evaluate the performance of word similarity computation. The two datasets both contain frequently-used Chinese word pairs with similarity scores annotated manually. We choose the Chinese Word Analogy dataset proposed by Chen et al. (2015) to evaluate the performance of word analogy inference, that is, \(\mathbf{w}(``king")-\mathbf{w}(``man")\simeq\mathbf{w}(``queen")-\mathbf{ w}(``woman")\).

Footnote 3: [https://github.com/Leonard-Xu/CWE/tree/master/data](https://github.com/Leonard-Xu/CWE/tree/master/data)

### Experimental Settings

We evaluate three SE-WRL models including S-SA, SAC and SAT on all tasks. As for baselines, we consider three conventional WRL models including Skip-gram, CBOW and GloVe. For Skip-gram and CBOW, we directly use the code released by Google Mikolov et al. (2013). GloVe is proposed by Pennington et al. (2014), which seeks the advantages of the WRL models based on statistics and those based on prediction. Moreover, we propose another model, Maximum Selection over Target Model (MST), for further comparison inspired by Chen et al. (2014). It represents the current word embeddings with only the most probable sense according to the contexts, instead of viewing a word as a particular distribution over all its senses similar to that of SAT.

For a fair comparison, we train these models with the same experimental settings and with their best parameters. As for the parameter settings, we set the context window size \(K=8\) as the upper bound, and during training, the window size is dynamically selected ranging from \(1\) to \(8\) randomly. We set the dimensions of word, sense and sememe embeddings to be the same \(200\). For learning rate \(\alpha\), its initial value is \(0.025\) and will descend through iterations. We set the number of negative samples to be \(25\). We also set a lower bound of word frequency as \(50\), and in the training set, those words less frequent than this bound will be filtered out. For SAT, we set \(K^{\prime}=2\).

### Word Similarity

The task of word similarity aims to evaluate the quality of word representations by comparing the similarity ranks of word pairs computed by WR-L models with the ranks given by dataset. WR-L models typically compute word similarities according to their distances in the semantic space.

#### 4.3.1 Evaluation Protocol

In experiments, we choose the cosine similarity between two word embeddings to rank word pairs. For evaluation, we compute the Spearman correlation between the ranks of models and the ranks of human judgments.

#### 4.3.2 Experiment Results

Table 1 shows the results of these models for word similarity computation. From the results we can observe that:

(1) Our SAT model outperforms other models, including all baselines, on both two test sets. This indicates that, by utilizing sememe annotation properly, our model can better capture the semantic relations of words, and learn more accurate word embeddings.

(2) The SSA model represents a word with the average of its sememe embeddings. In general, S-SA model performs slightly better than baselines, which tentatively proves that sememe information is helpful. The reason is that words which share common sememe embeddings will benefit from each other. Especially, those words with lower frequency, which cannot be learned sufficiently using conventional WRL models, in contrast, can

\begin{table}
\begin{tabular}{c c c} \hline \hline Model & Wordsim-240 & Wordsim-297 \\ \hline CBOW & 57.7 & 61.1 \\ GloVe & 59.8 & 58.7 \\ Skip-gram & 58.5 & 63.3 \\ \hline SSA & 58.9 & 64.0 \\ SAC & 59.0 & 63.1 \\ MST & 59.2 & 62.8 \\ SAT & **63.2** & **65.6** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results of word similarity computation.



obtain better word embeddings from SSA simply because their sememe embeddings can be trained sufficiently through other words.

(3) The SAT model performs much better than SSA and SAC. This indicates that SAT can obtain more precise sense distribution of a word. The reason has been mentioned above that, different from SAC using only one target word as attention for WSD, SAT adopts richer contextual information as attention for WSD.

(4) SAT works better than MST, and we can conclude that a soft disambiguation over senses prevents inevitable errors when selecting only one most-probable sense. The result makes sense because, for many words, their various senses are not always entirely different from each other, but share some common elements. In some contexts, a single sense may not convey the exact meaning of this word.

### Word Analogy

Word analogy inference is another widely-used task to evaluate the quality of WRL models [16].

#### 4.4.1 Evaluation Protocol

The dataset proposed by [20] consists of \(1,124\) analogies, which contains three analogy types: (1) capitals of countries (Capital), \(677\) groups; (2) states/provinces of cities (City), \(175\) groups; (3) family words (Relationship), \(272\) groups. Given an analogy group of words \((w_{1},w_{2},w_{3},w_{4})\), WRL models usually get \(\mathbf{w}_{2}-\mathbf{w}_{1}+\mathbf{w}_{3}\) equal to \(\mathbf{w}_{4}\). Hence for word analogy inference, we suppose \(w_{4}\) is missing, and WRL models will rank all candidate words according to their scores as follows:

\[R(w)=\cos(\mathbf{w}_{2}-\mathbf{w}_{1}+\mathbf{w}_{3},\mathbf{w}), \tag{10}\]

and select the top-ranked word as the answer.

For word analogy inference, we consider two evaluation metrics: (1) **Accuracy**. For each analogy group, a WRL model selects the top-ranked word \(w=\arg\max_{w}R(w)\), which is judged as positive if \(w=w_{4}\). The percentage of positive samples is regarded as the accuracy score for this WRL model. (2) **Mean Rank**. For each analogy group, a WRL model will assign a rank for the gold standard word \(w_{4}\) according to the scores computed by Eq. (10). We use the mean rank of all gold standard words as the evaluation metric.

#### 4.4.2 Experiment Results

Table 2 shows the evaluation results of these models for word analogy inference. From the table, we can observe that:

(1) The SAT model performs best among all models, and the superiority is more significant than that on word similarity computation. This indicates that SAT will enhance the modeling of implicit relations between word embeddings in the semantic space. The reason is that sememes annotated to word senses have encoded these word relations. For example, capital and Cuba are two sememes of the word "Havana", which provide explicit semantic relations between the words "Cuba" and "Havana".

(2) The SAT model does well on both classes of Capital and City, because some words in these classes have low frequencies, while their sememes occur so many times that sememe embeddings can be learned sufficiently. With these sememe embeddings, these low-frequent words can be learned more efficiently by SAT.

(3) It seems that CBOW works better than SAT on Relationship class. Whereas for the mean rank, CBOW gets the worst results, which indicates the performance of CBOW is unstable. On the contrary, although the accuracy of SAT is a bit lower than that of CBOW, SAT seldom gives an outrageous prediction. In most wrong cas

\begin{table}
\begin{tabular}{c|c c c c|c c c c} \hline \hline Model & \multicolumn{4}{c|}{Accuracy} & \multicolumn{4}{c}{Mean Rank} \\  & Capital & City & Relationship & All & Capital & City & Relationship & All \\ \hline CBOW & 49.8 & 85.7 & **86.0** & 64.2 & 36.98 & 1.23 & 62.64 & 37.62 \\ GloVe & 57.3 & 74.3 & 81.6 & 65.8 & 19.09 & 1.71 & 3.58 & 12.63 \\ Skip-gram & 66.8 & 93.7 & 76.8 & 73.4 & 137.19 & 1.07 & 2.95 & 83.51 \\ \hline SSA & 62.3 & 93.7 & 81.6 & 71.9 & 45.74 & 1.06 & 3.33 & 28.52 \\ SAC & 61.6 & 95.4 & 77.9 & 70.8 & 19.08 & 1.02 & **2.18** & 12.18 \\ MST & 65.7 & 95.4 & 82.7 & 74.5 & 50.29 & 1.05 & 2.48 & 31.05 \\ SAT & **83.2** & **98.9** & 82.4 & **85.3** & **14.42** & **1.01** & 2.63 & **9.48** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of word analogy inference.

 

es, SAT predicts the word "grandfather" instead of "grandmother", which is not completely nonsense, because in HowNet the words "grandmother", "grandfather", "grandma" and some other similar words share four common sememes while only one sememe of them are different. These similar sememes make the attention process less discriminative with each other. But for the wrong cases of CBOW, we find that many mistakes are about words with low frequencies, such as "stepdaughter" which occurs merely for \(358\) times. Considering sememes may relieve this problem.

### Case study

The above experiments verify the effectiveness of our models for WRL. Here we show some examples of sememes, senses and words for case study.

#### 4.5.1 Word Sense Disambiguation

To demonstrate the validity of Sememe Attention, we select three attention results in training set, as shown in Table 3. In this table, the first rows of three examples are word-sense-sememe structures of each word. For instance, in the third example, the word has two senses, _contingent_ and _troops_; _confingent_ has one sememe community, while _troops_ has one sememe army. The three examples all indicate that our models can estimate appropriate distributions of senses for a word given a context.

#### 4.5.2 Effect of Context Words for Attention

We demonstrate the effect of context words for attention in Table. 4. The word "Havana" consists of four sememes, among which two sememes capital and Cuba describe distinct attributes of the word from different aspects.

Here, we list three different context words "Cuba", "Russia" and "cigar". Given the context word "Cuba", both sememes get high weights, indicating their contributions to the meaning of "Havana" in this context. The context word "Russia" is more relevant to the sememe capital. When the context word is "cigar", the sememe Cuba has more influence, because cigar is a famous specialty of Cuba. From these examples, we can conclude that our Sememe Attention can accurately capture the word meanings in complicated contexts.

## 5 Conclusion and Future Work

In this paper, we propose a novel method to model sememe information for learning better word representations. Specifically, we utilize sememe information to represent various senses of each word and propose Sememe Attention to select appropriate senses in contexts automatically. We evaluate our models on word similarity and word analogy, and results show the advantages of our Sememe-Encoded WRL models. We also analyze several cases in WSD and WRL, which confirms our models are capable of selecting appropriate word senses with the favor of sememe attention.

We will explore the following research directions in future: (1) The sememe information in HowNet is annotated with hierarchical structure

\begin{table}
\begin{tabular}{c c c} \hline \hline Word & \multicolumn{2}{|l}{\%L/\#F(“Havana”)} \\ Sememe & [@] \&(capital) & ©(Cubea) \\ \hline �(“Cuba”) & 0.39 & 0.42 \\ �(“Russia”) & 0.39 & -0.09 \\ �(“cigar”) & 0.00 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sememe weight for computing attention.

\begin{table}
\begin{tabular}{c l|c|c} \hline \hline Word: \textbackslash{}^{+}\!\! 

and relations, which have not been considered in our framework. We will explore to utilize these annotations for better WRL. (2) We believe the idea of sememes is universal and could be well-functioned beyond languages. We will explore the effectiveness of sememe information for WRL in other languages.

## Acknowledgments

This work is supported by the 973 Program (No. 2014CB340501), the National Natural Science Foundation of China (NSFC No. 61572273, 61661146007), and the Key Technologies Research and Development Program of China (No. 2014BAK04B03). This work is also funded by the Natural Science Foundation of China (NSFC) and the German Research Foundation (DFG) in Project Crossmodal Learning, NSFC 61621136008 / DFC TRR-169.

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