Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation (2020)

Abstract: We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.

## introduction in the scientific literature, coordination is a common syntactic structure and is frequently used to describe technical terminologies. these coordinate structures often involve ellipsis, a linguistic phenomenon in which certain redundant words inferable from the context are omitted. for instance, the phrase "prostate cancer and breast cancer cells" conjoins two cell names, "prostate cancer cell" and "breast cancer cell," with the token "cell" eliminated from the first conjunct. this phenomenon raises significant challenges in named entity recognition (ner) tasks, and most of the current ner models @xcite can identify only non-elliptical conjuncts, e.g., "breast cancer cells," or incorrectly extract the whole coordinate phrases as single complex entities. therefore, identifying coordinated noun phrases is crucial to improving the model performance in nlp, particularly in ner and relation extraction tasks within in scientific domains. in this paper, we propose a simple yet effective method for finding coordination with related compound nouns, such as technical terms. compared to previous methods @xcite @xcite , our approach does not require any training on labeled data, and is applicable under the realistic conditions where annotations of coordinate structures are not readily available. our method employs recent pre-training language models such as elmo @xcite and bert @xcite to measure the similarities of words, and identifies coordination boundaries based on the property in which conjuncts share syntactic and semantic similarities. this property has been exploited in traditional alignment-based methods @xcite @xcite , and our system extends and simplifies such methods by using neural embedding representations instead of the handcrafted features or heuristic rules used in their approaches. our experiments show that, even without training, our method achieves good results in identifying nominal coordination boundaries in the genia corpus @xcite . when targeting only the coordination of noun phrases that do not contain clauses or prepositional phrases, our method is even comparable to a supervised baseline model trained on annotated data. ## related studies the goal of our method is to identify the coordination boundaries of noun phrases, including scientific named entities. in this respect, our work is similar in spirit to studies by @xcite and @xcite . @xcite proposed a supervised method trained on coordination-annotated data and resolved coordination ellipses included in biomedical named entities. in addition, @xcite proposed a dictionary-based method that resolves complex ellipses in coordinated noun phrases using linguistic rules and an entity mention dictionary. however, such annotated data or dictionaries are not readily available in practice. therefore, we propose a new method that does not require any training on the labeled data, and is applicable under realistic scenarios. in terms of the methodology, our study is highly inspired by the traditional alignment-based approaches for identifying the scopes of coordinate structures @xcite @xcite . their methods identify coordination boundaries by aligning similar words or phrasal units before and after a coordinator, assuming that the conjuncts share semantic and syntactic similarities. our method extends and simplifies their models by replacing their handcrafted features and heuristic rules with recent word embeddings. our method also differs from the studies by @xcite and @xcite trained the weights on handcrafted features using annotated data, whereas our method employs pre-trained word embeddings and does not require any training on the labeled data. given a sentence that consists of n words w 1:@xmath0, w 2 , ..., w n } and the coordinator w k , the goal of our method is to identify the span of the two noun conjuncts adjoined by w k . we presume that the first conjunct ends at w k-1 and the second conjunct starts at w k+1 . our model predicts a span (i, j|@xmath1, where w i is the beginning word of the first conjunct, and w j is the end word of the second conjunct. to achieve this, our method involves two procedures: preprocessing and sequence alignment. the pseudo-code is shown in algorithm 1. our method first suggests the candidates of noun conjuncts using a few simple rules and applies a sequence alignment to determine the best span. in the following, we describe each procedure in detail. ## conclusion in this study, we proposed a simple yet effective method for identifying the coordination boundary of noun phrases. our method identifies the coordination boundary by aligning words before and after a coordinator, assuming that they should share syntactic and semantic similarities. to calculate word similarities, our method exploits recent word embedding methods and finds the optimal alignment using the sequence alignment technique. our experiments on the genia corpus show that, without any training, our method can identify the coordination boundaries of noun phrases with good accuracy. when looking at the coordination that conjoins simple noun phrases, our method is comparable to the strong supervised model trained on annotated data. we also show that, using a few heuristic rules, our model can identify noun conjuncts with good recall and precision. this result suggests that our model can potentially be applied to more realistic problems, such as the resolution of ellipses in the scientific literature. in a future study, we plan to improve the performance of identifying such structures and integrate our method into the ner system. 7 we did not use the coordination-annotated penn treebank @xcite for the experiments due to the small number of elliptical coordination structures found in the data: its development data contain only 439 nominal coordination structures among a total of 848, and the number of elliptical coordination structures is as few as 74. there are also many terms that are ambiguous in term of whether they are a compound or not, such as "house ways and means committee".