Paraphrasing Compound Nominalizations (2021)

Abstract: A nominalization uses a deverbal noun to describe an event associated with its underlying verb. Commonly found in academic and formal texts, nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Our goal is to interpret nominalizations by generating clausal paraphrases. We address compound nominalizations with both nominal and adjectival modifiers, as well as prepositional phrases. In evaluations on a number of unsupervised methods, we obtained the strongest performance by using a pre-trained contextualized language model to re-rank paraphrase candidates identified by a textual entailment model.

## introduction nominalizations are widely used in academic and other genres of formal texts that adopt a compact and abstract writing style. a nominalization is a noun (e.g., "treatment") that is morphologically derived from a verb ("treat"), and that designates some aspects of the event referred to by the verb @xcite . because of the systematic correspondence between nominalization and clause structure, a noun phrase headed by a deverbal noun (e.g., "surgical treatment of fracture") often has a clausal paraphrase headed by the underlying verb ("the surgery treats fracture") @xcite . this paper aims to generate clausal paraphrases for nominalizations. a key requirement in this task is to map the arguments of the deverbal noun to those of the underlying verb. in the example above, the deverbal noun "treatment" has a prepositional phrase (pp) argument ("of fracture") that corresponds to the object of the verb "treat", while the prenominal modifier ("surgical") corresponds to the subject. this mapping varies according to the semantic context of the nominalization. an alternative nominalization, such as "fracture treatment with surgery", may have its pp argument ("with surgery") mapped to the subject, and its prenominal modifier ("fracture") mapped to the object. our task is distinguished from previous research in terms of both the input and output. in the field of nominalization disambiguation, most studies have focused on deverbal nouns with one argument, typically a nominal modifier @xcite . we extend the scope by addressing both nominal and adjectival modifiers, as well as pp argument. in terms of the output, most previous work assigned semantic role labels to arguments of the deverbal noun; in contrast, we generate paraphrases in predicate-argument form ( the rest of the paper is organized as follows. we define our task in the next section, and then review previous work in section 3. after a description of the dataset in section 4, we present our approach in section 5 and discuss experimental results in section 6. ## task definition as shown in type input: nominalization output: clausal paraphrase m a or m n n v p o mvo american influence on global culture america m influences v global culture o police response to the rioting the police m responds v to the rioting o ovm climatic effects of air pollutants air pollutants o affect v the climate m war preparations of the government the government o prepares v for war m vmo bodily injury to a friend injure v the body m of a friend o student admissions into universities admit v students m into universities o vom presidential nomination of harrison nominate v harrison o as president m business travel to greece travel v to greece o for business m mov naval assistance from italian powers navies m of italian powers o assist v majority decision of the panel a majority m of the panel o decides v the output is a clausal paraphrase, i.e., a predicate headed by a verb that corresponds to the deverbal noun n v . this verb may have a subject, object, or prepositional phrase corresponding to m a , m n or o. our model assumes that every input has a paraphrase. in actual deployment, the system would need to determine whether an input can be paraphrased, a task that we leave to future work. ## previous work our research is most closely related to noun compound interpretation (section 3.1) and nominal semantic role labeling (section 3.2). ## dataset we collected 369 sentences from english wikipedia that match the input pattern defined in section 2, i.e., a deverbal noun (n v ) modified by the preceding noun (m n ) or adjective (m a ), and by a prepositional phrase (pp). the m a must have a pertainym in wordnet; n v in the academic word list (coxhead, 2016) with high frequency are given priority, to reflect the widespread use of nominalizations in academic text. two annotators, a native speaker and a nearnative speaker of english, composed a paraphrase for each nominalization. a professor of linguistics who is a native speaker of english reviewed the paraphrases, either keeping both or selecting one of them. as shown in the system takes as input the original sentence and the strings n v , p, o, and m a or m n , ( ## experiment 6.1 evaluation metrics in a clausal paraphrase, the determiner and number of the noun (m ) that corresponds to the prenominal modifier (m a or m n ) can be ambiguous and open to different interpretations. in our evaluation, we removed all determiners and lemmatized all words in both the gold and the predicted paraphrase, and then compared them on two metrics: paraphrase accuracy the system is considered correct if the lemmatized form of the predicted paraphrase and gold paraphrase are identical. word order accuracy same as above, except that prepositions are not taken into consideration. this metric essentially measures the system's ability to predict the verb and arguments and to put them into the correct word order. gold arguments the system has access to the gold v , m and o. in other words, it needs only to determine the paraphrase type and prepositions. ## conclusion we have presented a study on generating clausal paraphrases for compound nominalizations. extending previous work, our dataset contains nominalizations with two arguments, namely a prepositional phrase argument as well as a nominal or adjectival modifier. we evaluated a number of unsupervised methods for paraphrase generation. experimental results show that using a textual entailment model, followed by re-ranking with a language model score, yields the best performance. the proposed method can contribute to downstream nlp tasks that require natural language understanding of texts in which nominalizations are frequently found.