C o R ec: An Easy Approach for Coordination Recognition (2023)

Abstract: In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.

## introduction coordination is a widely observed syntactic phenomenon in sentences across diverse corpora. based on our counting, 39.4% of the sentences in ontonotes release 5.0 @xcite contain at least one coordination. the frequently appeared conjunctive sentences bring many challenges to various nlp tasks, including natural language inference (nli) @xcite , named entity recognition (ner) @xcite , and text simplification @xcite . specifically, in open information extraction (open ie) tasks, researchers find that ineffective processing of conjunctive sentences will result in substantial yield lost @xcite @xcite , where yield is essential since open ie tasks aim to obtain a comprehensive set of structured information. thus processing conjunctive sentences is important to improve the performance of open ie models. it is a common practice to apply constituency parsers or dependency parsers to identify the coordination structures of a sentence. however, there are several drawbacks. first, the state-of-the-art syntactic parsers confront an increase of errors when processing conjunctive sentences, especially when the input sentence contains complex coordination structures. second, applying parsers can be slow, which will make the identification of coordination less efficient. existing coordination boundary detection methods rely on the results of syntactic parsers @xcite @xcite and thus still face similar drawbacks. in this work, we approach the coordination recognition problem without using syntactic parsers and propose a simple yet effective pipeline model coordination recognizer (corec). corec composes of two steps: coordinator identification and conjunct boundary detection. for coordinator identification, we consider three types of coordinator spans: contiguous span coordinators (e.g. 'or' and 'as well as'), paired span coordinators (e.g. 'either...or...'), and coordination with 'respectively'. given each identified coordinator span, we formulate the conjunct boundary detection task as a sequence labeling task and design a position-aware bioc labeling schema based on the unique characteristics of this task. we also present a simple trick called coordinator markers that can greatly improve the model performance. despite corec's simplicity, we find it to be both effective and efficient in the empirical studies: corec consistently outperforms state-of-theart models on benchmark datasets from both general domain and biomedical domain. further experiments demonstrate that processing the conjunctive sentences with corec can enhance the yield of open ie models. in summary, our main contributions are: ## related work for the tasks of coordination boundary detection and disambiguation, earlier heuristic, non-learningbased approaches design different types of features and principles based on syntactic and lexical analysis @xcite @xcite @xcite . @xcite are the first to propose a neural-network-based model for coordination boundary detection. this model operates on top of the constituency parse trees, and decomposes the trees to capture the syntactic context of each word. @xcite design similarity and replaceability feature vectors and train scoring models to evaluate the possible boundary pairs of the conjuncts. since these methods are designed to work on conjunct pairs, they have natural shortcomings to handle more than two conjuncts in one coordination. researchers in the open information extraction domain also consider coordination analysis to be important to improve model performance. calm, proposed by @xcite , improves upon the conjuncts identified from dependency parsers. it ranks conjunct spans based on the 'replaceability' principle and uses various linguistic constraints to additionally restrict the search space. openie6 @xcite also has a coordination analyzer called igl-ca, which utilizes a novel iterative labeling-based architecture. however, its labels only focus on the boundaries of the whole coordination and do not utilize the position information of the specific conjuncts. ## experiments training setup the proposed corec is trained on the training set (wsj 0-18) of penn treebank foot_1 @xcite following the most common split, and wsj 19-21 are used for validation and wsj 22-24 for testing. the ground truth constituency parse trees containing coordination structures are pre-processed to generate labels for the two sub-tasks as follows. if a constituent is tagged with 'cc' or 'conjp', then it is considered a coordinator span. for each coordinator span, we first extract the constituents which are siblings to the coordinator span, and each constituent is regarded as a conjunct coordinated by that coordinator span. we automatically generate labels as described in section 3.2. we also manually check and correct labels for complicated cases. testing setup we use three testing datasets to evaluate the performance of the proposed corec model. the first dataset, ontonotes, contains 1,000 randomly selected conjunctive sentences from the english portion of ontonotes release 5.0 foot_2 @xcite . the second dataset, genia, contains 802 conjunctive sentences from the testing set of genia foot_3 @xcite , a benchmark dataset from biomedical domain. the third dataset, penn, contains 1,625 conjunctive sentences from penn treebank testing set (wsj 22-24). these three datasets contain the gold standard constituency parsing annotations. we convert them into the oc and bioc labels in the same way as described in section 4. each testing dataset is further split into a 'simple' set and a 'complex' set based on the complexity of the coordinators. 'simple' set contains instances with 'and', 'but', 'or' as target coordinators only and these three coordinators can be handled by all baselines. whereas the 'complex' set contains other scenarios including multi-token contiguous span coordinators (e.g. 'as well as'), the paired span coordinators (e.g., 'not only...but also...'), and coordination with 'respectively'. 'complex' instances may not be hard to predict. for example, the instances with paired span coordinators may be easier for the model since the first coordinator span may give better clues about the boundaries of conjuncts. baseline methods we compare the proposed corec with two categories of baseline methods: parsing-based and learning-based methods. parsing-based methods convert the constituency parsing results and regard constituents at the same depth with the target coordinator spans as coordinated conjuncts. we adopt two state-of-the-art constituency parsers, allennlp @xcite and stanford corenlp foot_4 parsers, for this category. for learning-based methods, we choose two state-ofthe-art models for coordination boundary detection, teranishi+19 @xcite , and igl-ca @xcite . all results are obtained using their official released code. evaluation metrics we evaluate both the effectiveness and efficiency of different methods. we evaluate effectiveness using span level precision, recall, and f1 scores. a predicted span is correct only if it is an exact match of the corresponding span in the ground truth. for efficiency evaluation, we report the inference time of each method. all experiments are conducted on a computer with intel(r) core(tm) i7-11700k 3.60ghz cpu, nvidia(r) rtx(tm) 3070 gpu, and 40gb memory. ## conclusions in this paper, we develop corec, a simple yet effective coordination recognizer without using syntactic parsers. we approach the problem by formulating a pipeline of coordinator identification and conjunct boundary detection. corec can not only detect the boundaries of more than two coordinated conjuncts but also handle multiple coordinations in one sentence. it can also deal with both simple and complex cases of coordination. experiments show that corec outperforms state-of-the-art models on datasets from various domains. further experiments imply that corec can improve the yield of state-of-the-art open ie models.