## 2 Materials and Methods
In many cases, these limitations can be attributed to the absence of standard terminology for the possible concepts. This is especially the case in many complex and rapidly developing industries, such as semiconductor manufacturing. Here, domain experts typically describe the concepts using short text, which comprises domain-specific language and many abbreviations. In comparison to other proposed approaches concerned with FME cells' consistency, most approaches are only concerned with the consistency of failure modes to achieve a standardized description. Our research is concerned with the effect, root causes, and failure modes. However, before addressing the standardization of all FMEA cells, we noticed cases of merged cells, which we address in part of this paper. Similar to [12], we opted for a data-driven approach that requires limited labeling effort. In the case of identifying merged cells, multiple indicators could be devised. For example, in the case of individual cells containing entire causal chains, causal relation extraction from texts could be of value. In [16], the authors survey causal relation extraction from text and describe the most relevant approaches. They distinguish between explicit and implicit causal relations. For the case of FMEA documents, the causal relations are most likely to occur explicitly, i.e., the causal relation is articulated explicitly in the short text of the FMEA cell. Thus, we propose to leverage causal patterns identified as in [17] to identify merged cells containing causal cues. The first indicator (i.e., causal cues) and the proposed methods to detect mainly assumes consistent use of cues through the data (which is typically not the case) and does not cover cases (b), (c), (d) of merged cells. Thus, it yields high precision, but limited recall. As a response, we build upon the work presented in [18], leveraging the Mutual Information (MI) to detect merged cells. The proposed approach to extract the intra-sentential patterns, which are meaningful combinations of terms (e.g., \"voltage threshold\", \"leakage current \", etc. ), from FMEA cells. To construct patterns, It calculates MI between the terms based on their occurrence in the text of FMEA cells. The proposed approach is based on six steps, explained as follow:  
**Step 1** FMEA cells' short text n_grams: In this step, the algorithm splits the FMEA short text into a list of terms based on spaces. The algorithm collects all the possible n_grams (i.e., sequence of terms of a length n) that occurred in the FMEA cells' short text. **Step 2** n_grams occurrence extraction: This step counts the FMEA cells where an n-gram occurred in its short texts. Figure 2: The four categories of merged cells: (**a**) merged cells containing a causal chain; (**b**) merged cell as a result of confounding bias; (**c**) merged cell as a result of bias caused by conditioning on a collider; (**d**) merged cells caused by describing the interaction between concepts. **Step 3 n_grams Mutual Information calculation: This step calculates the collected n-grams Mutual Information based on the occurrence of n_gram terms in FMEA cells (i.e., based on the occurrences in FMEA cells). Hence, an n_gram with higher Mutual Information is more likely to represent a pattern. Whereas high Mutual Information indicates a large reduction in uncertainty, low Mutual Information indicates a small reduction, and zero Mutual Information between a set of terms means the terms are independent. The Mutual Information is calculated based on the occurrence of the terms in the FMEA cell using the equation below:**  
\[MI(t_{1},t_{2},...,t_{n})=P(t_{1},t_{2},...,t_{n})\log\frac{P(t_{1},t_{2},..., t_{n})}{\prod_{i=1}^{i=n}P(t_{i})}\]  
**Step 4 Patterns extraction (n_gram filtering): The collected n_grams are filtered based on two criteria (their number of occurrences in FMEA cells and their Mutual Information). Thus, this approach is performed using two hyperparameters: the Mutual Information threshold (**MIth**) and the occurrences threshold (**OCH**).