## 3 Integrated Model for MA and NER  
For \(n\) characters, \(C_{1,n}\), in a sentence \(S\), let \(M_{1,n}\) and \(NE_{1,n}\) denote a morpheme tag sequence and an NE tag sequence in \(S\), respectively. Table 2 shows morpheme tags and NE tags that are defined according to the character-level BIO (beginner-inner-outer) tagging scheme. \begin{table}
\begin{tabular}{c c} \hline \hline
**NLP Step** & **Incorrect Results** \\ \hline MA & u-ri/NP + eun-haeng/NNG + e/KB ga/VV + da/EF \\ NER & N/A \\ \hline
**NLP Step** & **Correct Results** \\ \hline MA & u-ri-eun-haeng/NNP + e/JKB ga/VV + da/EF \\ NER & u-ri-eun-haeng/ORG \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of Korean named entities affected by morphological analysis (MA) results (NP, NNG, JKB, VV, and EF are Korean part-of-speech (POS) tags, and ORG is a Korean named entity (NE) tag). The integrated model, known as (MANE), can then be formally expressed using the following equation:  
\[MANE(S)\overset{def}{=}\underset{M_{1,n},NE_{1,n}}{\arg}\underset{C_{1,n}}{\max} P(M_{1,n},NE_{1,n}|C_{1,n}) \tag{1}\]  
According to the chain rule, (1) can be rewritten as the following equation:  
\[MANE(S)\overset{def}{=}\underset{M_{1,n},NE_{1,n}}{\arg}\underset{C_{1,n}}{ \max}P(M_{1,n}|C_{1,n})P(NE_{1,n}|C_{1,n},M_{1,n}) \tag{2}\]  
To obtain the sequence labels \(M_{1,n}\) and \(E_{1,n}\) that maximize (2), we adopted a bidirectional long short-term memory with a label attention network (Bi-LSTM-LAN), as shown in Figure 1.  
MANE comprises two layers of Bi-LSTM-LAN: an MA layer, shown on the left, and an NER layer, shown on the right. The input unit of the MA layer is a character, and each character is represented by a concatenation of three types of embeddings: character, alphabet, and feature embeddings, as shown in Figure 2.  
In Figure 2, \(C_{i}\) is the \(i\)-th character in a sentence, and \(E_{c}(C_{i})\) is a character embedding of \(C_{i}\). Each character embedding is represented by a randomly initialized \(n\)-dimensional vector and fine-tuned during training. To render MANE robust to typographical errors, we additionally represent each character through an alphabet embedding. A Korean character consists of a first consonant called _disung_, a vowel called _joonsung_, and a final consonant called _jongsung_ that can be omitted. For example, in the word \"hak-kyo (school)\", the first character \"hak\" comprises three alphabets; \"h\" called _choosing_, \"a\" called _joonsung_, and \"k\" called _jongsung_. On the other hand, the second character \"kyo\" comprises two alphabets; \"k\" called _choosing_ and \"yo\" called _joonsung_. In Figure 2, \(E_{a}\Big{(}C_{i}^{j}\Big{)}\) is an alphabet embedding of the \(j\)-th alphabet in \(C_{i}\) that comprises the maximum of three alphabets in \begin{table}
\begin{tabular}{c c} \hline \hline
**Morpheme Tag** & **Descriptions** \\ \hline B-(NOUN\(|\)AD\(|\)ADV\(|\)...) & Beginner of a morpheme with the POS following “B-” \\ I-(NOUN\(|\)AD\(|\)ADV\(|\)...) & Inner of a morpheme with the POS following “I-” \\ O & Outer of any morphemes \\ \hline
**NE Tag** & **Descriptions** \\ \hline B-(PER\(|\)LOC\(|\)ORG) & Beginner of an NE with the category following “B-” \\ I-(PER\(|\)LOC\(|\)ORG) & Inner of an NE with the category following “I-” \\ O & Outer of any NEs \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Morpheme tags and NE tags. Figure 1: Overall architecture of MANE. Korean, and each alphabet embedding is represented in the same manner as the character embeddings. The maximum three alphabet embeddings are passed into a convolutional neural network (CNN) with 100 filters (filter widths: 1, 2, and 3) [15]. In NER, dictionary look-up features-- which are used to check whether there is an input word in a preconstructed NE dictionary--significantly affect the performance. Based on [PERSON]'s study [9], in which effective dictionary look-up features have been proposed for Korean NER, we adopted the same dictionary look-up features in MANE.