our method can outperform other well-known readability assessment methods,and the classic text-based word embedding models on all the datasets.By concatenating our knowledge-enriched word embedding with the hand-crafted features,the performance can be further improved. The rest of the paper is organized as follows.Section 2 provides the related work for readability assessment.Section 3 describes the details of KEWE.Section 4 presents the experiments and results. Finally Section 5 concludes the paper with future work. 2 Related Work In this section,we briefly introduce three research topics relevant to our work:readability assessment, word embedding,and graph embedding. Readability Assessment.The researches on readability assessment have a relatively long history from the beginning of last century(Collinsthompson,2014).Early studies mainly focused on designing readability formulas to evaluate the reading scores of texts.Some of the well-known readability formulas include the SMOG formula(McLaughlin,1969),the FK formula (Kincaid et al.,1975),and the Dale- Chall formula(Chall,1995).At the beginning of the 21th century,supervised approaches have been introduced and then explored for readability assessment(Si and Callan,2001;Collins-Thompson and Callan,2004;Schwarm and Ostendorf,2005).Researchers have focused on improving the performance by designing highly effective features (Pitler and Nenkova,2008;Heilman et al.,2008;Feng et al.,2010; Vajjala and Meurers,2012)and employing effective classification models (Heilman et al.,2007;Kate et al.,2010;Ma et al.,2012;Jiang et al.,2015;Cha et al.,2017).While most studies are conducted for English,there are studies for other languages,such as French(Francois and Fairon,2012),German (Hancke et al.,2012),Bangla (Sinha et al.,2014),Basque(Gonzalez-Dios et al.,2014),Chinese (Jiang et al.,2014),and Japanese (Wang and Andersen,2016). Word Embedding.Researchers have proposed various methods on word embedding,which mainly include two broad categories:neural network based methods(Bengio et al.,2003;Collobert et al.,2011; Mikolov et al.,2013)and co-occurrence matrix based methods(Turney and Pantel,2010;Levy and Gold- berg,2014b;Pennington et al.,2014).Neural network based methods learn word embedding through training neural network models,which include NNLM(Bengio et al.,2003),C&W(Collobert and We- ston,2008).and word2vec (Mikolov et al..2013).Co-occurrence matrix based methods learn word embedding based on the co-occurrence matrices,which include LSA (Deerwester,1990),Implicit Ma- trix Factorization (Levy and Goldberg,2014b),and GloVe (Pennington et al.,2014).Besides the general word embedding learning methods,researchers have also proposed methods to learn word embedding to include certain properties (Liu et al.,2015;Shen and Liu,2016)or for certain domains (Tang et al., 2014:Ren et al..2016:Alikaniotis et al..2016:Wu et al.,2017). Graph embedding.Graph embedding aims to learn continuous representations of the nodes or edges based on the structure of a graph.The graph embedding methods can be classified into three categories (Goyal and Ferrara,2017):factorization based (Roweis and Saul,2000;Belkin and Niyogi,2001), random walk based (Perozzi et al.,2014;Grover and Leskovec,2016),and deep learning based(Wang et al.,2016).Among them,the random walk based methods are easy to comprehend and can effectively reserve the centrality and similarity of the nodes.Deepwalks(Perozzi et al.,2014)and node2vec(Grover and Leskovec,2016)are two representatives of the random walk based methods.The basic idea of Deepwalk is viewing random walk paths as sentences,and feeding them to a general word embedding model.node2vec is similar to Deepwalk,although it simulates a biased random walk over graphs,and often provides efficient random walk paths 3 Learning Knowledge-Enriched Word Embedding for Readability Assessment In this section,we present the details of Knowledge-Enriched Word Embedding(KEWE)for readability assessment.By incorporating the word-level readability knowledge,we extend the existing word embed- ding model and design two models with different learning structures.As shown in Figure 1,the above one is the knowledge-only word embedding model(KEWE)which only takes in the domain knowledge, 367367 our method can outperform other well-known readability assessment methods, and the classic text-based word embedding models on all the datasets. By concatenating our knowledge-enriched word embedding with the hand-crafted features, the performance can be further improved. The rest of the paper is organized as follows. Section 2 provides the related work for readability assessment. Section 3 describes the details of KEWE. Section 4 presents the experiments and results. Finally Section 5 concludes the paper with future work. 2 Related Work In this section, we briefly introduce three research topics relevant to our work: readability assessment, word embedding, and graph embedding. Readability Assessment. The researches on readability assessment have a relatively long history from the beginning of last century (Collinsthompson, 2014). Early studies mainly focused on designing readability formulas to evaluate the reading scores of texts. Some of the well-known readability formulas include the SMOG formula (McLaughlin, 1969), the FK formula (Kincaid et al., 1975), and the Dale￾Chall formula (Chall, 1995). At the beginning of the 21th century, supervised approaches have been introduced and then explored for readability assessment (Si and Callan, 2001; Collins-Thompson and Callan, 2004; Schwarm and Ostendorf, 2005). Researchers have focused on improving the performance by designing highly effective features (Pitler and Nenkova, 2008; Heilman et al., 2008; Feng et al., 2010; Vajjala and Meurers, 2012) and employing effective classification models (Heilman et al., 2007; Kate et al., 2010; Ma et al., 2012; Jiang et al., 2015; Cha et al., 2017). While most studies are conducted for English, there are studies for other languages, such as French (Franc¸ois and Fairon, 2012), German (Hancke et al., 2012), Bangla (Sinha et al., 2014), Basque (Gonzalez-Dios et al., 2014), Chinese (Jiang et al., 2014), and Japanese (Wang and Andersen, 2016). Word Embedding. Researchers have proposed various methods on word embedding, which mainly include two broad categories: neural network based methods (Bengio et al., 2003; Collobert et al., 2011; Mikolov et al., 2013) and co-occurrence matrix based methods (Turney and Pantel, 2010; Levy and Gold￾berg, 2014b; Pennington et al., 2014). Neural network based methods learn word embedding through training neural network models, which include NNLM (Bengio et al., 2003), C&W (Collobert and We￾ston, 2008), and word2vec (Mikolov et al., 2013). Co-occurrence matrix based methods learn word embedding based on the co-occurrence matrices, which include LSA (Deerwester, 1990), Implicit Ma￾trix Factorization (Levy and Goldberg, 2014b), and GloVe (Pennington et al., 2014). Besides the general word embedding learning methods, researchers have also proposed methods to learn word embedding to include certain properties (Liu et al., 2015; Shen and Liu, 2016) or for certain domains (Tang et al., 2014; Ren et al., 2016; Alikaniotis et al., 2016; Wu et al., 2017). Graph embedding. Graph embedding aims to learn continuous representations of the nodes or edges based on the structure of a graph. The graph embedding methods can be classified into three categories (Goyal and Ferrara, 2017): factorization based (Roweis and Saul, 2000; Belkin and Niyogi, 2001), random walk based (Perozzi et al., 2014; Grover and Leskovec, 2016), and deep learning based (Wang et al., 2016). Among them, the random walk based methods are easy to comprehend and can effectively reserve the centrality and similarity of the nodes. Deepwalks (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016) are two representatives of the random walk based methods. The basic idea of Deepwalk is viewing random walk paths as sentences, and feeding them to a general word embedding model. node2vec is similar to Deepwalk, although it simulates a biased random walk over graphs, and often provides efficient random walk paths. 3 Learning Knowledge-Enriched Word Embedding for Readability Assessment In this section, we present the details of Knowledge-Enriched Word Embedding (KEWE) for readability assessment. By incorporating the word-level readability knowledge, we extend the existing word embed￾ding model and design two models with different learning structures. As shown in Figure 1, the above one is the knowledge-only word embedding model (KEWEk) which only takes in the domain knowledge