Causality-aware Concept Extraction based on Knowledge-guided Prompting
Year: 2023

Abstract:
Concepts benefit natural language understanding but are far from complete in existing knowledge graphs (KGs). Recently, pre-trained language models (PLMs) have been widely used in text-based concept extraction (CE). However, PLMs tend to mine the co-occurrence associations from massive corpus as pre-trained knowledge rather than the real causal effect between tokens. As a result, the pre-trained knowledge confounds PLMs to extract biased concepts based on spurious co-occurrence correlations, inevitably resulting in low precision. In this paper, through the lens of a Structural Causal Model (SCM), we propose equipping the PLM-based extractor with a knowledgeguided prompt as an intervention to alleviate concept bias. The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts. Our extensive experiments on representative multilingual KG datasets justify that our proposed prompt can effectively alleviate concept bias and improve the performance of PLM-based CE models. The code has been released on https://github.com/ siyuyuan/KPCE.

heading: 1 Introduction
text: The concepts in knowledge graphs (KGs) enable machines to understand natural languages better, and thus benefit many downstream tasks, such as question answering (Han et al., 2020), commonsense reasoning (Zhong et al., 2021) and entity typing (Yuan et al., 2022). However, the concepts, especially the fine-grained ones, in existing KGs still need to be completed. For example, in the widely used Chinese KG CN-DBpedia (Xu et al., 2017), there are nearly 17 million entities but only 0.27 million concepts in total, and more than 20% entities even have no concepts. Although Probase (Wu
∗Corresponding authors.
Abstract of Louisa May Alcott
Louisa May Alcott was an American novelist, short story writer and poet best known as the author of the novel Little Women and its sequels Little Men and Jo's Boys.
PLM-based Extraction
Models
Biased Concept Correct Concept
Louisa May Alcott is not a novel!
Figure 1: The example of concept bias. The PLM-based CE models are biased to extract novel mistakenly as the concept of Louisa May Alcott from the text.
et al., 2012) is a large-scale English KG, the finegrained concepts with two or more modifiers in it only account for 30% (Li et al., 2021). We focus on extracting multi-grained concepts from texts to complete existing KGs.
Most of the existing text-based concept acquisition approaches adopt the extraction scheme, which can be divided into two categories: 1) patternmatching approaches (Auer et al., 2007; Wu et al., 2012; Xu et al., 2017), which can obtain high-quality concepts but only have low recall due to poor generalization; 2) learning-based approaches (Luo et al., 2020; Ji et al., 2020; Yuan et al., 2021a), which employ pre-trained language models (PLMs) fine-tuned with labeled data to extract concepts.
However, an unignorable drawback of these learning-based approaches based on PLMs is concept bias. Concept bias means the concepts are extracted based on their contextual (co-occurrence) associations rather than the real causal effect between the entities and concepts, resulting in low extraction precision. For example, in Figure 1, PLMs tend to extract novel and writer together as concepts for the entity Louisa May Alcott even if we explicitly input the entity Louisa May Alcott to the model. Previous work demonstrates that causal inference is a promising technique for bias analy-
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sis (Lu et al., 2022). To analyze the reasons behind concept bias, we devise a Structural Causal Model (SCM) (Pearl, 2009) to investigate the causal effect in the PLM-based concept extraction (CE) system, and show that pre-trained knowledge in PLMs confounds PLMs to extract biased concepts. During the pre-training, the entities and biased concepts (e.g., Louisa May Alcott and novel) often co-occur in many texts. Thus, PLMs tend to mine statistical associations from a massive corpus rather than the real causal effect between them (Li et al., 2022), which induces spurious co-occurrence correlations between entities (i.e., Louisa May Alcott) and biased concepts (i.e., novel). Since we cannot directly observe the prior distribution of pre-trained knowledge, the backdoor adjustment is intractable for our problem (Pearl, 2009). Alternatively, the frontdoor adjustment (Peters et al., 2017) can apply a mediator as an intervention to mitigate bias.
In this paper, we adopt language prompting (Gao et al., 2021; Li and Liang, 2021) as a mediator for the frontdoor adjustment to handle concept bias. We propose a novel Concept Extraction framework with Knowledge-guided Prompt, namely KPCE to extract concepts for given entities from text. Specifically, we construct a knowledge-guided prompt by obtaining the topic of the given entity (e.g., person for Louisa May Alcott) from the knowledge in the existing KGs. Our proposed knowledge-guided prompt is independent of pre-trained knowledge and fulfills the frontdoor criterion. Thus, it can be used as a mediator to guide PLMs to focus on the right cause and alleviate spurious correlations. Although adopting our knowledge-guided prompt to construct the mediator is straightforward, it has been proven effective in addressing concept bias and improving the extraction performance of PLMbased extractors in the CE task.
In summary, our contributions include: 1) To the best of our knowledge, we are the first to identify the concept bias problem in the PLM-based CE system. 2) We define a Structural Causal Model to analyze the concept bias from a causal perspective and propose adopting a knowledge-guided prompt as a mediator to alleviate the bias via frontdoor adjustment. 3) Experimental results demonstrate the effectiveness of the proposed knowledge-guided prompt, which significantly mitigates the bias and achieves a new state-of-the-art for CE task.

heading: 2 Related Work
text: Concept Acquisition Most of the existing textbased concept acquisition approaches adopt the extraction scheme, which can be divided into two categories: 1) Pattern-matching Approaches: extract concepts from free texts with hand-crafted patterns (Auer et al., 2007; Wu et al., 2012; Xu et al., 2017). Although they can obtain high-quality concepts, they have low recall due to their poor generalization ability; 2) Learning-based Approaches: mostly employ the PLM-based extraction models from other extraction tasks, such as the Named Entity Recognition (NER) models (Li et al., 2020; Luo et al., 2021; Lange et al., 2022) and Information Extraction models (Fang et al., 2021; Yuan et al., 2021a) in the CE task. Although they can extract many concepts from a large corpus, the concept bias cannot be well handled.
Causality for Language Processing Several recent work studies causal inference combined with language models for natural language processing (NLP) (Schölkopf, 2022), such as controllable text generation (Hu and Li, 2021; Goyal et al., 2022) and counterfactual reasoning (Chen et al., 2022; Paranjape et al., 2022). In addition, causal inference can recognize spurious correlations via Structural Causal Model (SCM) (Pearl, 2009) for bias analysis and eliminate biases using causal intervention techniques (Weber et al., 2020; Lu et al., 2022). Therefore, there are also studies showing that causal inference is a promising technique to identify undesirable biases in the NLP dataset (Feder et al., 2022) pre-trained language models (PLMs) (Li et al., 2022). In this paper, we adopt causal inference to identify, understand, and alleviate concept bias in concept extraction.
Language Prompting Language prompting can distill knowledge from PLMs to improve the model performance in the downstream task. Language prompt construction methods can be divided into two categories (Liu et al., 2021a): 1) Hand-crafted Prompts, which are created manually based on human insights into the tasks (Brown et al., 2020; Schick and Schütze, 2021; Schick and Schutze, 2021). Although they obtain high-quality results, how to construct optimal prompts for a certain downstream task is an intractable challenge; 2) Automated Constructed Prompts, which are generated automatically from natural language phrases (Jiang et al., 2020; Yuan et al., 2021b) or vector space (Li
and Liang, 2021; Liu et al., 2021b). Although previous work analyzes the prompt from a causal perspective (Cao et al., 2022), relatively little attention has been paid to adopting the prompt to alleviate the bias in the downstream task.

heading: 3 Concept Bias Analysis
text: In this section, we first formally define our task. Then we investigate the concept bias issued by PLMs in empirical studies. Finally, we devise a Structural Causal Model (SCM) to analyze the bias and alleviate it via causal inference.

heading: 3.1 Preliminary
text: Task Definition Our CE task addressed in this paper can be formulated as follows. Given an entity E = {e1, e2, · · · , e|E|} and its relevant text T = {t1, t2, · · · , t|T |} where ei (or ti) is a word token, our framework aims to extract one or multiple spans from T as the concept(s) of E.
Data Selection It must guarantee that the given text contains concepts. The abstract text of an entity expresses the concepts of the entity explicitly, which can be obtained from online encyclopedias or knowledge bases. In this paper, we take the abstract text of an entity as its relevant text T . The details of dataset construction will be introduced in § 5.1. Since we aim to extract concepts from T for E, it is reasonable to concatenate E and T to form the input text X = {E, T}.

heading: 3.2 Empirical Studies on Concept Bias
text: To demonstrate the presence of concept bias, we conduct empirical studies on the CN-DBpedia dataset (Xu et al., 2017). First, we randomly sample 1 million entities with their concepts from CNDBpedia, and select the top 100 concepts with the
most entities as the typical concept set. Then we randomly select 100 entities with their abstracts for each typical concept to construct the input texts and run a BERT-based extractor to extract concepts. Details of the extraction process will be introduced in § 4.2. We invite volunteers to assess whether the extracted concepts are biased. To quantify the degree of concept bias, we calculate the bias rate of concept A to another concept B. The bias rate is defined as the number of entities of A for which B or the sub-concepts of B are mistakenly extracted by the extractor, divided by the total number of entities of A.
The bias rates among 26 typical concepts are shown in Figure 2, where the concepts (dots) of the same topic are clustered in one rectangle. The construction of concept topics will be introduced in § 4.1. From the figure, we can conclude that concept bias is widespread in the PLM-based CE system and negatively affects the quality of the results. Previous studies have proven that causal inference can analyze bias via SCM and eliminate bias with causal intervention techniques (Cao et al., 2022). Next, we will analyze concept bias from a causal perspective.

heading: 3.3 The Causal Framework for Concept Bias Analysis
text: The Structural Causal Model We devise a Structural Causal Model (SCM) to identify the causal effect between the input text X of a given entity E and the concept span S that can be ex-
tracted from X . As shown in Figure 3, our CE task aims to extract one or multiple spans S from X as the concept(s) of E where the causal effect can be denoted as X → S.
During the pre-training, the contextual embedding of one token depends on the ones that frequently appear nearby in the corpus. We extrapolate that the high co-occurrence between the entities of true concepts (e.g., writer) and biased concepts (e.g., novel) in the pre-trained knowledge induces spurious correlations between entities (e.g., Louisa May Alcott) and biased concepts (e.g., novel). Therefore, the PLM-based CE models can mistakenly extract biased concepts even if the entity is explicitly mentioned in X . The experiments in § 5.4 also prove our rationale. Based on the foregoing analysis, we define the pre-trained knowledge K from PLM-based extraction models as a confounder.
We cannot directly observe the latent space of the PLMs, and thus the backdoor adjustment (Pearl, 2009) is not applicable in our case. Alternatively, we adopt the frontdoor adjustment (Peters et al., 2017) and design a mediator to mitigate the concept bias.
Causal Intervention To mitigate the concept bias, we construct a prompt P as a mediator for X → S, and then the frontdoor adjustment can apply do-operation.
Specifically, to make the PLMs attend to the right cause and alleviate spurious co-occurrence correlation (e.g., novel and Louisa May Alcott), we assign a topic as a knowledge-guided prompt P (i.e., person) to the input text X (The detailed operation is elaborated in § 4.1). The topics obtained from KGs are independent of pre-trained knowledge, and thus P fulfills the frontdoor criterion.
For the causal effect X → P , we can observe that X → P → S ← K is a collider that blocks the association between P and K, and no backdoor path is available for X → P . Therefore, we can directly rely on the conditional probability after applying the do-operator for X:
P (P = p|do(X = x)) = P (P = p|X = x). (1)
Next, for the causal effect P → S, P ← X ← K → S is a backdoor path from P to S, which we need to cut off. Since K is an unobserved variable,
we can block the backdoor path through X:
P (S|do(P )) = ∑
x
P (S|P,X = x)P (X = x).
(2) Therefore, the underlying causal mechanism of our CE task is a combination of Eq.1 and Eq.2, which can be formulated as:
P (S|do(X)) = ∑
p
P (S|p, do(X))P (p|do(X))
= ∑
p
P (S|do(P ), do(X))P (p|do(X))
= ∑
p
P (S|do(P ))P (p|do(X)). (3)
The theoretical details of the frontdoor adjustment are introduced in Appendix A.
We make the assumption of strong ignorability, i.e., there is only one confounder K between X and S. One assumption of the frontdoor criterion is that the only way the input text X influences S is through the mediator P . Thus, X → P → S must be the only path. Otherwise, the front-door adjustment cannot stand. Notice that K already represents all the knowledge from pre-trained data in PLMs. Therefore, it is reasonable to use the strong ignorability assumption that it already includes all possible confounders.
Through the frontdoor adjustment, we can block the backdoor path from input text to concepts and alleviate spurious correlation caused by the confounder, i.e., pre-trained knowledge. In practice, we can train a topic classifier to estimate Eq.1 (§ 4.1) and train a concept extractor on our training data to estimate Eq.2 (§ 4.2). Next, we will introduce the implementation of the frontdoor adjustment in detail.

heading: 4 Methodology
text: In this section, we present our CE framework KPCE and discuss how to perform prompting to alleviate concept bias. The overall framework of KPCE is illustrated in Figure 4, which consists of two major modules: 1) Prompt Constructor: assigns the topic obtained from KGs for entities as a knowledge-guided prompt to estimate Eq.1; 2) Concept Extractor: trains a BERT-based extractor with the constructed prompt to estimate Eq.2 and extract multi-grained concepts from the input text. Next, we will introduce the two modules of KPCE.

heading: 4.1 Prompt Constructor
text: Knowledge-guided Prompt Construction To reduce the concept bias, we use the topic of a given entity as a knowledge-guided prompt, which is identified based on the external knowledge of the existing KGs. Take CN-DBpedia (Xu et al., 2017) as an example 1. We randomly sample one million entities from this KG and obtain their existing concepts. Then, we select the top 100 concepts having the most entities to constitute the typical concept set, which can cover more than 99.80% entities in the KG. Next, we use spectral clustering (Von Luxburg, 2007) with the adaptive K-means (Bhatia et al., 2004) algorithm to cluster these typical concepts into several groups, each of which corresponds to a topic. To achieve the spectral clustering, we use the following overlap coefficient (Vijaymeena and Kavitha, 2016) to measure the similarity between two concepts,
Overlap(c1, c2) = |ent(c1) ∩ ent(c2)|
min(|ent(c1)|, |ent(c2)|) + δ (4)
where ent(c1) and ent(c2) are the entity sets of concept c1 and concept c2, respectively. We then construct a similarity matrix of typical concepts to achieve spectral clustering. To determine the best number of clusters, we calculate the Silhouette Coefficient (SC) (Aranganayagi and Thangavel, 2007) and the Calinski Harabaz Index (CHI) (Maulik and Bandyopadhyay, 2002) from 3 to 30 clusters. The scores are shown in Figure 5, from which we find that the best number of clusters is 17. As a result, we cluster the typical concepts into 17 groups and define a topic name for each group. The 17 typical topics and their corresponding concepts are listed in Appendix B.1
1In fact, the concepts of CN-DBpedia are inherited from Probase, so the typical topics are the same for CN-DBpedia and Probase.
Identifying Topic Prompt for Each Entity We adopt a topic classifier to assign the topic prompt to the input text X , which is one of the 17 typical topics in Table 6. To construct the training data, we randomly fetch 40,000 entities together with their abstract texts and existing concepts in the KG. According to the concept clustering results, we can assign each topic to the entities. We adopt transformer encoder (Vaswani et al., 2017) followed by a two-layer perception (MLP) (Gardner and Dorling, 1998) activated by ReLU, as our topic classifier 2. We train the topic classifier to predict the topic prompt P = {p1, p2, · · · , p|P |} for X , which is calculated as 3:
P = argmax i
( P (P i|X) ) , 1 ≤ i ≤ 17, (5)
where P i is the i-th topic among the 17 typical topics.
In our experiments, the topic classifier achieves more than 97.8% accuracy in 500 samples by human assessment. Through training the topic classifier, we can estimate Eq.1 to identify the causal effect X → P .

heading: 4.2 Concept Extractor
text: Prompt-based BERT The concept extractor is a BERT equipped with our proposed prompt followed by a pointer network (Vinyals et al., 2015). The pointer network is adopted for extracting multigrained concepts.
We first concatenate the token sequence with the tokens of P and X to constitute the input, i.e., {[CLS]P[SEP]X[SEP]}, where [CLS] and [SEP] are the special tokens in BERT. With multiheaded self-attention operations over the above in-
2We do not employ the PLM-based topic classifier since it will bring a direct path from K to P in Figure 3.
3The detailed training operation of topic classifier can be found in Appendix B.1
put, the BERT outputs the final hidden state (matrix), i.e., HNL ∈ R(|P |+|X|+3)×d′ where d′ is the vector dimension and NL is the total number of layers. Then the pointer network predicts the probability of a token being the start position and the end position of the extracted span. We use pstart,pend ∈ R|P |+|X|+3 to denote the vectors storing the probabilities of all tokens to be the start position and end position, which are calculated as
[pstart;pend] = softmax(HNLW +B) (6)
where B ∈ R(|P |+|X|+3)×2 and W ∈ Rd′×2 and are both trainable parameters. We only consider the probabilities of the tokens in the abstract T . Given a span with xi and xj as the start token and the end token, its confidence score csij ∈ R can be calculated as
csij = p start i + p end j . (7)
Accordingly, the model outputs a ranking list of candidate concepts (spans) with their confidence scores. We only reserve the concepts with confidence scores bigger than the selection threshold. An example to illustrate how to perform the pointer network is provided in Appendix B.2.
During training, the concept extractor is fed with the input texts with topic prompts and outputs the probability (confidence scores) of the spans, and thus can estimate the causal effect P → S in Eq.2. Model Training We adopt the cross-entropy function CE(·) as the loss function of our model. Specifically, suppose that ystart ∈ N|P |+|X|+3 (or yend ∈ N|P |+|X|+3) contains the real label (0 or 1) of each input token being the start (or end) position of a concept. Then, we have the following two training losses for the predictions:
Lstart = CE(pstart,ystart), (8) Lend = CE(pend,yend). (9)
Then, the overall training loss is
L = αLstart + (1− α)Lend (10)
where α ∈ (0, 1) is the control parameter. We use Adam (Kingma and Ba, 2015) to optimize L.

heading: 5 Experiments

heading: 5.1 Datasets
text: CN-DBpedia From the latest version of Chinese KG CN-DBpedia (Xu et al., 2017) and Wikipedia,
we randomly sample 100,000 instances to construct our sample pool. Each instance in the sample pool consists of an entity with its concept and abstract text 4. Then, we sample 500 instances from the pool as our test set and divide the rest of the instances into the training set and validation set according to 9:1.
Probase We obtain the English sample pool of 50,000 instances from Probase (Wu et al., 2012) and Wikipedia. The training, validation and test set construction are the same as the Chinese dataset.

heading: 5.2 Evaluation Metrics
text: We compare KPCE with seven baselines, including a pattern-matching approach i.e., Hearst pattern. Detailed information on baselines and some experiment settings is shown in Appendix C.1 and C.2. Some extracted concepts do not exist in the KG, and cannot be assessed automatically. Therefore, we invite the annotators to assess whether the extracted concepts are correct. The annotation detail is shown in Appendix C.3.
Please note that the extracted concepts may already have existed in the KG for the given entity, which we denote as ECs (existing concepts). However, our work expects to extract correct but new concepts (that do not exist in the KG) to complete the KGs, which we denote as NCs (new concepts). Therefore, we record the number of new concepts (NC #) and display the ratio of correct concepts (ECs and NCs) as precision (Prec.). Since it is difficult to know all the correct concepts in the input text, we report the relative recall (RecallR). Specifically, suppose NCs # is the total number of new concepts extracted by all models. Then, the relative recall is calculated as NC # divided by NCs # 5. Accordingly, the relative F1 (F1R) can be calculated with Prec. and RecallR. In addition, we also record the average length of new concepts (LenNC) to investigate the effectiveness of the pointer network.

heading: 5.3 Overall Performance
text: We present the main results in Table 1. Generally, we have the following findings:
Our method outperforms previous baselines by large margins, including previous state-of-the-art (MRC-CE, Yuan et al., 2021a). However, the
4If one entity has multiple concepts, we randomly select one as the golden label.
5Please note that NCs # is counted based on all models in one comparison. Therefore, RecallR can be different for one model when the compared models change.
pattern-based approach still beats the learningbased ones in precision, envisioning a room for improvement. We find that KPCE achieves a more significant improvement in extracting new concepts, indicating that KPCE can be applied to achieve KG completion (§ 5.5). We also compare KPCE with its ablated variant and the results show that adding a knowledge-guided prompt can guide BERT to achieve accurate CE results.
We notice that almost all models have higher extraction precision on the Chinese dataset than that on the English dataset. This is because the modifiers are usually placed before nouns in Chinese syntactic structure, and thus it is easier to identify these modifiers and extract them with the coarse-grained concepts together to form the finegrained ones. However, for the English dataset, not only adjectives but also subordinate clauses modify coarse-grained concepts, and thus identifying these modifiers is more difficult.
Compared with learning-based baselines, KPCE can extract more fine-grained concepts. Although the Hearst pattern can also extract fine-grained concepts, it cannot simultaneously extract multigrained concepts when a coarse-grained concept term is the subsequence of another fine-grained concept term. For example, in Figure 4, if Hearst Pattern extracts American novelist as a concept, it cannot extract novelist simultaneously. KPCE solves this problem well with the aid of the pointer network and achieves a much higher recall.

heading: 5.4 Analysis
text: In response to the motivations of KPCE, we conduct detailed analyses to further understand KPCE and why it works.
How does KPCE alleviate the concept bias? As mentioned in § 3.2, the concept bias occurs primarily among 26 concepts in CN-DBpedia. To justify that KPCE can alleviate concept bias with the aid of prompts, we randomly select five concepts and run KPCE with its ablated variant to extract concepts for 100 entities randomly selected from each of the five concepts. Then we calculate the bias rates of each concept, and the results in Table 2 show that KPCE has a much lower bias rate than the vanilla BERT-based concept extractor. Thus, the knowledge-guided prompt can significantly mitigate the concept bias.
Furthermore, a case study for an entity Korean alphabet is shown in Table 3. We find that the
proposed prompts can mitigate the spurious cooccurrence correlation between entities and biased concepts by decreasing the confidence scores of biased concepts (i.e., language and alphabet) and increasing the scores of correct concepts (i.e., system and writing system). Thus, the knowledge-guided prompt can significantly alleviate the concept bias and result in more accurate CE results.
How does the prompt affect the spurious cooccurrence correlations? To explore the rationale behind the prompt-based mediator, we focus on the attention distribution for the special token [CLS], since it is an aggregate representation of the sequence and can capture the sentence-level semantic meaning (Devlin et al., 2019; Chang et al., 2022). Following previous work (Clark et al., 2019), we calculate the attention probabilities of
[CLS] to other tokens by averaging and normalizing the attention value in 12 attention heads in the last layers. The attention distributions of the KPCE and its ablation variant are visualized in Figure 6. We find that the tokens of writer and novel both have high attentions in the vanilla BERT-based concept extractor. However, after adopting our knowledge-guided prompt, the attention probabilities of novel is lower than before, and thus can help the model to reduce the spurious co-occurrence correlations derived from pre-trained knowledge.
What if other knowledge injection methods are adopted? We claim that the topics obtained from external KGs are better than the keyword-based topics from the text on guiding BERT to achieve our CE task. To justify it, we compare KPCE with another variant, namely KPCE LDA, where the topics are the keywords obtained by running Latent Dirichlet Allocation (LDA) (Blei et al., 2001) over the abstracts of all entities. Besides, we also compare KPCE with ERNIE (Zhang et al., 2019), which implicitly learns the knowledge of entities during pre-training. The detail about LDA and ERNIE is shown in Appendix C.4. The comparison results are listed in Table 4. It shows that our design of the knowledge-guided prompt in KPCE exploits the value of external knowledge more thoroughly than the two remaining schemes, thus achieving
better CE performance.

heading: 5.5 Applications
text: KG Completion We run KPCE for all entities existing in CN-DBpedia to complement new concepts. KPCE extracts 7,623,111 new concepts for 6 million entities. Thus, our framework can achieve a large-scale concept completion for existing KGs.
Domain Concept Acquisition We collect 117,489 Food & Delight entities with their descriptive texts from Meituan 6, and explore two application approaches. The first is to directly apply KPCE, and the second is to randomly select 300 samples as a small training set to fine-tune KPCE. The results in Table 5 show that: 1) The transfer ability of KPCE is greatly improved with the aid of prompts; 2) KPCE can extract high-quality concepts in the new domain only with a small portion of training samples. Furthermore, after running directly, KPCE extracts 81,800 new concepts with 82.66% precision. Thus, our knowledge-guided prompt can significantly improve the transfer ability of PLMs on the domain CE task.

heading: 6 Conclusion
text: In this paper, we identify the concept bias in the PLM-based CE system and devise a Structural
6http://www.meituan.com, a Chinese e-business platform.
Causal Model to analyze the bias. To alleviate concept bias, we propose a novel CE framework with knowledge-guided prompting to alleviate spurious co-occurrence correlation between entities and biased concepts. We conduct extensive experiments to justify that our prompt-based learning framework can significantly mitigate bias and has an excellent performance in concept acquisition.

heading: 7 Limitations
text: Although we have proven that our work can significantly alleviate concept bias and extract highquality and new concepts, it also has some limitations. In this section, we analyze three limitations and hope to advance future work.
Model Novelty Although KPCE can effectively mitigate the spurious co-occurrence correlations between entities and biased concepts, the proposed framework is not entirely novel. The novelty of our work is to conduct the first thorough causal analysis that shows the spurious correlations between entities and biased concepts in the concept extraction task. After defining the problem and SCM of concept extraction in § 3.1, we propose a promptbased approach to implement the interventions toward the SCM to elicit the unbiased knowledge from PLMs. Previous work in language prompting mostly guides the PLMs with prompts but is unaware of the cause-effect relations in its task, which may hinder the effectiveness of prompts. We hope our work can inspire future work to utilize language prompting from a causal perspective.
Topic Classification Although the topics obtained by clustering are mostly mutually exclusive, there are still cases where an entity can be classified into multiple topics. Therefore, considering only one topic for the entity excludes the correct concepts.
Threshold Selection We only reserve concepts with confidence scores bigger than the selection threshold (§ 4.2), which can hardly achieve a satisfactory balance of precision and recall. If we select a relatively big threshold, we can get more accurate concepts but may lose some correct ones. If the recall is preferred, precision might be hurt.
We suggest that future work consider these three limitations to achieve better performance in the CE task.

heading: Acknowledgement
text: We would like to thank the anonymous reviewers for their valuable comments and suggestions for this work. This work is supported by the Chinese NSF Major Research Plan (No.92270121), Shanghai Science and Technology Innovation Action Plan (No.21511100401) and the Science and Technology Commission of Shanghai Municipality Grant (No. 22511105902).

heading: A Theoretical Details of Causal Framework
text: A.1 Preliminaries SCM The Structural Causal Model (SCM) is associated with a graphical causal model to describe the relevant variables in a system and how they interact with each other. An SCM G = {V, f} consists of a set of nodes representing variables V , and a set of edges between the nodes as functions f to describe the causal relations. Figure 3 shows the SCM that describes the PLM-based CE system. Here the input text X serves as the treatment, and the extracted concept span S is the outcome. In our SCM, pre-trained knowledge K is a cause of both X and S, and thus K is a confounder. A confounder can open backdoor paths (i.e., X ← K → S) and cause a spurious correlation between X and S. To control the confounding bias, intervention techniques with the do-operator can be applied to cut off backdoor paths.
Causal Intervention To identify the true causal effects of X → S, we can adopt the causal intervention to fix the input X = x and removes the correlation between X and its precedents, denoted as do(X = x). In this way, the true causal effects of X → S can be represented as P (S = s|do(X = x)). The backdoor adjustment and the frontdoor adjustment are two operations to implement interventions and obtain P (S = s|do(X = x)).
Next, we will elaborate on the details of the two operations.
A.2 The Backdoor Adjustment The backdoor adjustment is an essential tool for causal intervention. For our SCM, the pre-trained knowledge blocks the backdoor path between X and S, then the causal effect of X = x on S can be calculated by:
P (S = s|do(X = x)) = Pm(S = s|X = x) = ∑
k
Pm(S = s|X = x,K = k)Pm(K = k)
= ∑
k
P (S = s|X = x,K = k)P (K = k),
(11)
where Pm is the probability after applying the dooperator, and P (K = k) needs to be estimated from data or priorly given. However, it is intractable to observe the pre-training data and obtain
the prior distribution of the pre-trained knowledge. Therefore, the back adjustment is not applicable in our case.
A.3 The Frontdoor Adjustment
The frontdoor adjustment is a complementary approach to applying the intervention when we cannot identify any set of variables that obey the backdoor adjustment.
In our SCM, we aim to estimate the direct effect of X on S, while being unable to directly measure pre-trained knowledge K. Thus, we introduce a topic prompt P as a mediator, and then the frontdoor adjustment can adopt a two-step do-operation to mitigate bias.
Step 1 As illustrated in § 3.3, we first analyze the causal effect X → P . Since the collider, i.e., X → P → S ← K blocks the association between P and K, there is no backdoor path from X to P . Thus we can obtain the conditional probability as (same as Eq.1):
P (P = p|do(X = x)) = P (P = p|X = x). (12)
To explain Step 1, we take an entity Louisa May Alcott with her abstract as an example. We can assign the topic person as a prompt to make the PLM-based extractor alleviate spurious correlation between Louisa May Alcott and novel, and concentrate on extracting person-type concepts.
Step 2 In this step, we investigate the causal effect P → S. P ← X ← K → S contains a backdoor from P to S. Since the data distribution of X can be observed, we can block the backdoor path through X:
P (S = s|do(P = p)) = ∑
x
P (S = s|X = x, P = p)P (X = x),
(13)
where P (X = x) can be obtained from the distribution of the input data, and P (S = s|X = x, P = p) is the conditional probability of the extracted span given the abstract with a topic prompt, which can be estimated P (S = s|X = x, P = p) by the concept extractor.
Now we can chain the two steps to obtain the
causal effect X → S:

heading: B Detailed Information about KPCE
text: B.1 Identifying Topic for Each Entity
The 17 typical topics and their corresponding concepts are listed in Table 6. We predict the topic of the entity as one of the 17 typical topics using a transformer encoder-based topic classifier. We randomly fetch 40,000 entities together with their existing concepts in the KG. According to the concept clustering results, we can assign each topic to the entities. Specifically, we concatenate E and X as input to the classifier. With multi-headed selfattention operation over the input token sequence, the classifier takes the final hidden state (vector) of a token [CLS], i.e., hNL[CLS] ∈ Rd ′′ , to compute the topic probability distribution P (T i|E,X) ∈ R17, where NL is the total number of layers and d′′ is the vector dimension. Then, we identify the topic with the highest probability T as the topic of X ,
which is calculated as follows,
H0 = EW0 +B0, (15)
Hl = encoder(Hl−1), 1 ≤ l ≤ NL, (16) P (T ) = softmax(hNL[CLS]W L), (17)
T = argmax i
( P (T i) ) , 1 ≤ i ≤ 17 (18)
where E ∈ R(|E|+|X|)×d is the random initial embedding matrix of all input tokens and d is the embedding size. Hl ∈ R(|E|+|X|)×d′′ is the hidden matrix of the l-th layer. hNL[CLS] is obtained from H
NL . Furthermore, W0 ∈ Rd×d′′ ,B0 ∈ R(|E|+|X|)×d′′ and WL ∈ Rd′′×17 are both training parameters.
B.2 An Example for Point Network
As mentioned in § 4.2, we adopt a point network to achieve multi-grained concept extraction (Yuan et al., 2021a). The model generates a ranking list of candidate concepts (spans) along with their confidence scores, and outputs the concepts with confidence scores bigger than the selection threshold. Note that one span may be output repeatedly as the same subsequence of multiple extracted concepts through an appropriate selection threshold.
For example, as shown in Figure 7, writer is extracted multiple times as the subsequence of three different granular concepts when the confidence score threshold is set to 0.30. Therefore, the point network enables our framework to extract multigrained concepts.

heading: C Experiment Detail
text: C.1 Baselines
We compare KPCE with seven baselines. Most of the compared models are the extraction models feasible for extraction tasks, including Named Entity Recognition (NER), Relation Extraction (RE), and Open Information Extraction (Open IE). In addition, we also compare the pattern-based approach. However, we do not compare ontology extension models and generation models, since both do not meet our scenario. Since entity typing models cannot find new concepts, they are also excluded from our comparison. Please note that, except MRCCE, other baselines applied in concept extraction cannot extract multi-grained concepts.
• Hearst (Jiang et al., 2017): With specific handwritten rules, this baseline can extract concepts from free texts. We design 5 Hearst patterns listed in Table 7 where we translate the Chinese patterns for the Chinese dataset into English.
• FLAIR (Akbik et al., 2019): It is a novel NLP framework that combines different words and document embeddings to achieve excellent results. FLAIR can also be employed for concept extraction since it can extract spans from the text.
• XLNet (Yang et al., 2020): With the capability of modeling bi-directional contexts, this model can extract clinical concepts effectively.
• KVMN (Nie et al., 2020): As a sequence labeling model, KVMN is proposed to handle NER by leveraging different types of syntactic information through the attentive ensemble.
• XLM-R (Conneau et al., 2020; Lange et al., 2022): It is a Transformer-based multilingual masked language model incorporating XLM (Conneau and Lample, 2019) and RoBERTa (Liu et al., 2019), which has proven to be effective in extracting concepts.
• BBF (Luo et al., 2021): It is an advanced version of BERT built with Bi-LSTM and CRF. With optimal token embeddings, it can extract high-quality medical and clinical concepts.
C.2 Experiment Settings Our experiments are conducted on a workstation of dual GeForce GTX 1080 Ti with 32G memory and the environment of torch 1.7.1. We adopt a BERTbase with 12 layers and 12 self-attention heads as the topic classifier and concept extractor in KPCE. The training settings of our topic classifier are: d = 768, batch size = 16, learning rate = 3e-5, dropout rate = 0.1 and training epoch = 2. The training settings of our concept extractor are: d = 768, m = 30, batch size = 4, learning rate = 3e-5, dropout rate = 0.1 and training epoch = 2. The α in Eq.8 is set to 0.3 and the selection threshold of candidate spans in the concept extractor is set to 0.12 based on our parameter tuning.
C.3 Human Assessment Some extracted concepts do not exist in the KG, which cannot be automatically assessed. Therefore, we invite some volunteers to assess whether the extracted concepts are correct for the given entities. We denote an extracted concept as an EC (existing concept) that has already existed in the KG for the given entity. We denote an extracted concept as an NC (new concept) represents a correct concept not
existing in the KG for the given entity. We employ four annotators in total to ensure the quality of the assessment. All annotators are native Chinese and proficient in English. Each concept is labeled with 0, 1 or 2 by three annotators, where 0 means a wrong concept for the given entity, while 1 and 2 represent EC and NC, respectively. If the results from the three annotators are different, the fourth annotator will be hired for a final check. We protect the privacy rights of the annotators and pay the annotators above the local minimum wage.
C.4 Other Knowledge Injection Methods As we mentioned before, the topics of the knowledge-guided prompt come from external KGs, which are better than the keyword-based topics from the text on guiding BERT to achieve accurate concept extraction.
To justify it, we compared KPCE with another variant, namely KPCE LDA, where the topics are the keywords obtained by running Latent Dirichlet Allocation (LDA) (Blei et al., 2001) over all entities’ abstracts. Specifically, the optimal number of LDA topic classes was also determined as 17 through our tuning study. For a given entity, its topic is identified as the keyword with the highest probability of its topic class. Besides, we also compared KPCE with ERNIE. ERNIE (Zhang et al., 2019) adopts entity-level masking and phrase-level masking to learn language representation. During pre-training of ERNIE, all words of the same entity mentioned or phrase are masked. In this way, ERNIE can implicitly learn the prior knowledge of phrases and entities, such as relationships between entities and types of entities, and thus have better generalization and adaptability.
The comparison results are listed in Table 4, which shows that our design of the knowledgeguided prompt in KPCE exploits external knowledge’s value more thoroughly than the rest two schemes.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Limitations

heading: 3 A2. Did you discuss any potential risks of your work?
text: Limitations

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Abstract and Section 1

heading: 3 A4. Have you used AI writing assistants when working on this paper?
text: Check grammar for the whole paper
B 3 Did you use or create scientific artifacts? Section 5.1

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Sections 5.1

heading: 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Section 5.1

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 5.1
7 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Our data is collected from the existing KGs, and there is no offensive content.

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Section 5.1
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 5.1
C 3 Did you run computational experiments? Section 5

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Appendix C.2
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Appendix C.2

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5.2
7 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? We do not use existing packages
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants?

heading: Appendix C.3

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Appendix C.3

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Appendix C.3

heading: 3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? Appendix C.3
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. We do not have any human subjects research.

heading: 3 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: Appendix C.3

heading: Future Work
text: In this section, we analyze three limitations and hope to advance future work. Model Novelty Although KPCE can effectively mitigate the spurious co-occurrence correlations between entities and biased concepts, the proposed framework is not entirely novel. The novelty of our work is to conduct the first thorough causal analysis that shows the spurious correlations between entities and biased concepts in the concept extraction task. After defining the problem and SCM of concept extraction in § 3. 1, we propose a promptbased approach to implement the interventions toward the SCM to elicit the unbiased knowledge from PLMs. Previous work in language prompting mostly guides the PLMs with prompts but is unaware of the cause-effect relations in its task, which may hinder the effectiveness of prompts. We hope our work can inspire future work to utilize language prompting from a causal perspective. Topic Classification Although the topics obtained by clustering are mostly mutually exclusive, there are still cases where an entity can be classified into multiple topics. Therefore, considering only one topic for the entity excludes the correct concepts. Threshold Selection We only reserve concepts with confidence scores bigger than the selection threshold (§ 4. 2), which can hardly achieve a satisfactory balance of precision and recall. If we select a relatively big threshold, we can get more accurate concepts but may lose some correct ones. If the recall is preferred, precision might be hurt. We suggest that future work consider these three limitations to achieve better performance in the CE task