## 3 Training Data Collection
### Command Structured Parsing Algorithm and Details
#### 3.2.1 Semantic Parsing Using TF-IDF Keyword Extraction Combined with Glove Word Vectors  
This section introduces the key indicator extraction process of simple natural language instructions and complex natural language instructions. In this paper, we consider simple natural language instructions as sub-instructions of complex natural language instructions, and we first cut complex natural language instructions into simple natural language instructions. Then we use the TF-IDF algorithm to extract keywords from the instructions, fill the extracted key indicators into the AOM language structure according to the lexical features, and use the word vector generated by Glove to perform the similarity optimization of the extracted information. The basic workflow is shown in Figure 3.  
* Step (1) represents the Chinese word separation of the input Chinese natural language commands. Unlike languages such as English, where there are spaces separating each word, there is no separation between each word in Chinese; word separation is required before subsequent processing. * Step (2) performs lexical annotation, and in the lexical annotation result, the terms marked as /c are concatenations in the commands. Parallel conjunctions connect words, phrases, sentences, etc., such as \"then\", \"and\", \"our\", \"so\", and so on. Currently, the presence of sub-instructions is determined by considering the presence or absence of concatenation in the input natural language commands. In the proposed method, if coordinating conjunctions such as \"and\", \"our\", and \"then\" are found later, the command is considered a complex natural language command. * Step (3) represents stop word filtering and the removal of some redundant information. * Step (4) represents the extraction of key indicator information using the TF-IDF keyword extraction process; the details are described as follows. First, lexical annotation is performed for natural language commands to limit the range of word attributes and improve the effectiveness of keyword extraction. In this paper, the lexical annotation is based on the lexical annotation set of BYU. A dictionary containing 98 words is defined to match the lexical properties, and the lexical annotation is completed while the words are divided. Finally, keyword extraction is completed based on lexical annotation. The main purpose is to extract the relevant attributes describing the controlled object in the command, which is achieved by stop-word elimination, word frequency-inverse text frequency algorithm, and word sense similarity calculation. Term  
Figure 3: Workflow for the extraction of basic natural language commands. Frequency (TF), which indicates the frequency of the keyword, w, in the document, Di, is calculated as:  
\[TF_{w_{i}d_{i}}=\frac{count(w)}{|D_{i}|} \tag{1}\]  
where \(count(w)\) denotes the number of occurrences of keyword \(w\), and \(|D|\) is the number of all words in document \(D_{i}\). * Step (5) represents the optimization process of similarity calculation; for example, in this subsection, \"# \(\&\)\" (\"open\") may not be recognized, so the Glove word vector model is used to vectorize it, calculate the cosine similarity, and find the most similar synonyms for replacement. After repeated tests, the two words with cosine similarity greater than 0.68 are synonyms. The cosine similarity of \"\(\ddagger\)\" (\"turn on\") and \"\(\#\)\(\&\)\" (\"open\") is 0.87, so they are identified as synonyms; for the word vectors \(x_{i}\) and \(y_{i}\), the cosine similarity is calculated as follows, where the cosine similarity takes the value interval of [0,1]. In Equation (2), the closer the cosine value of the angle between the two word vectors is to 1, the closer the semantics of these two words are.