Finding The Right One and Resolving it (2020)

Abstract: One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is. In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments - once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks. We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution. Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.

## introduction one-anaphora is an anaphoric relation between a non-lexical proform (i.e. one or ones) and the head noun or the nominal group inside a noun phrase (np). consider the example sentence in (1) from the british national @xcite , where the word one can be easily understood as room, from the preceding context. one-anaphora can represent a particular case of identity-of-sense anaphora where the anaphor shares only the sense of the antecedent and not the complete reference. this category of one-anaphora is named as sense sharing one-anaphors as opposed to "contrastive anaphors" presented in (1) @xcite , where the "lower" room that the anaphor refers to is in contrast with the the "upper" room as antecedent. such an interpretation can also be vague in some cases, as in (2), where the bomb story that the mother is telling might in fact be the best bomb story in the competition, but not necessarily. in other cases, it is possible that the entity that the anaphor one refers to is a subset of the entities the antecedent denotes, such as in (3), where the black car that jack liked is actually one amongst the many cars that he saw. this category of one-anaphora is discussed by some linguists as "member anaphora" for representative sampling @xcite , or "nominal substitutes" that stand in for a meaningful head @xcite . the antecedent can also be the head noun with its propositional argument, such as in (4), where the anaphor resolves as point of agreement. sometimes, the antecedent boundary selection decision is vague, even for human evaluators. for instance, the antecedent in (5) can be presentation on global warming or just presentation, depending on the context. however, for a sentence like (6), there is little ambiguity that the antecedent is only the head noun book without its prepositional argument. it will be absurd for the anaphor to be interpreted as book with yellow cover, although a sloppy reading such as this is also possible. ## previous work the nominal anaphoric element one is extensively discussed in theoretical linguistics as one-anaphora, noun anaphora, one-insertion, one-substitution and pronominalization @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . in computational linguistics literature, however, it has largely been ignored, despite the evident impact of one-anaphora resolution in improving the accuracy of downstream natural language processing (nlp) tasks such as machine translation (mt) and question answering (qa). to the best of our knowledge, the earliest computational approach to one-anaphora detection and resolution comes from @xcite , who presented several linguistically-motivated heuristics to distinguish one-anaphora from other non-anaphoric uses of one in english. for the resolution task, she used web search to select potential antecedent candidates. the second seminal work comes from @xcite that uses gardiner's heuristics as features to train a machine learning (ml) model. the most recent work on one-anaphora comes from @xcite where it has been treated as one of the several sense anaphoric relations in english. the authors create sananotes corpus where they annotate one third of the ontonotes corpus for sense anaphora. they use a support vector machine (svm) classifier -liblinear implementation @xcite along with 31 lexical and syntactic features, to distinguish between the anaphoric and the non-anaphoric class. trained and tested on one-third of the ontonotes dataset annotated as the sananotes corpus, their system achieves 61.80% f1 score on the detection of all anaphoric relations, including one-anaphora. their baseline statistical model outeperforms the existing ml model for one-anaphora detection. this work, however, only limits itself to the detection part, deeming resolution of sense anaphora as a hard nlp task. ## getting to know every one english has three distinct lexemes spelled as onethe regular third person indefinite pronoun, the indefinite cardinal numeral (determinative) and regular common count noun. there is no visible difference in their orthographic base form. however, they are totally different with respect to their morphological, syntactic, and semantic properties. on the surface, this difference can be observed in the way these forms inflect (morphology), behave in a sentence (syntax) and impart meaning (semantics) @xcite . previous efforts to classify the word one in english involve classification based on different functions of the word in discourse-numeric, partitive, anaphoric, generic, idiomatic, and unclassifiable; and in terms of the type of antecedent the anaphoric one takes-a kind, a set, an individual instance @xcite @xcite . this scheme has been extended for classification of other sense anaphoric relations as well @xcite . this distinction clubs closely related types like numeric and partitive (both are determinative, roughly mean "1") to different classes. it also treats the regular count noun anaphora and determinative anaphora together as the anaphoric class. this makes the previous research miss important underlying linguistic generalisations in these forms. in syntactic literature, one-anaphora refers to an anaphoric instance of the word one, where its syntactic properties resemble that of a count noun @xcite . like an en- ## corpus creation in this section, we explain our efforts to build a one-anaphora corpus that contains actual instances of one-anaphora and is sizeable enough for training supervised machine learning models. we make this process easier by using linguistic theory on syntactic environment of one-anaphora. to begin with, since one-anaphora is a count noun, we select all plural ones as plurality is a feature of count nouns. for the singular form, we identify five pos string sequences that capture the syntactic distribution of one-anaphora in english. the basic idea is that one-anaphora, being a regular count noun, will always occur inside of an np. in other words, it will be proceeded by a determiner or noun modifier like category and could be followed by a relative clause. all the syntactically possible combinations for one-anaphora to exist are presented in for our annotation purpose, we use the british national corpus that contains over one hundred million words of british english, drawn from written and spoken sources. the text comes from a variety of sources like books, periodicals, media, letters, conversations and monologues. the text also has part of speech tags assigned by the claws part-of-speech tagger @xcite . to fetch potential one-anaphora, we perform a semi-automatic search using the pos string templates discussed above. to calculate accuracy of our pos string templates, we check their output on 5000 randomly selected sentences containing the word ones or ones . our templates retrieve 153 positive sentences. we manually check all the 5000 sentences and do not find any one-anaphora instance missed by the templates. however, of the 153 results, 18 are incorrect (false positives). hence, we get a full recall, a precision of 88.24 and f1 score of 93.75. although the precision is slightly low and the high f1 score is mainly contributed from the prediction of 4,847 negative instances correctly, these results show that the templates are good enough to fetch a variety of oneanaphora candidates that can be followed by manual confirmation. this is also much less expensive than previous entirely manual annotation efforts. a simple search for the word ones and ones in the bnc yields 2,72,469 results. we run the templates on these sentences, which yields 15,647 unique matches. of these, we manually check the first 1058 sentences only. 3 we keep the true positive cases for the final corpus. from these 1058 sentences, we get 912 positive sentences containing 921 one-anaphora. for these 921 anaphors, we look for antecedents. since the distance between the one-anaphor and antecedent is generally not that large @xcite , for finding and marking the antecedents, we only consider a context of up to three sentences, including the current sentence. if an antecedent is not present within this context or is not present at all endophorically, we leave the anaphor without its resolution marked. this decision speeds up the annotation effort. ## one-anaphora resolution in this section, we describe a framework to resolve one-anaphora in free text. we break the complete task in two subtasks -the first being the detection of the anaphor and the second the selection of the antecedent candidate from its context. see figure 2 for an overview of the framework. ## conclusion in this paper, we used the most recent linguistic understanding of the word one in english to define and classify the one-anaphora phenomenon for computational linguistics research. we built a big corpus containing actual instances of one-anaphora by hand annotating sentences from bnc and used the annotations to experiment with state-of-the-art neural models for one-anaphora detection and resolution. for word and context representation, we experimented with pre-trained fasttext and bert word embeddings. we achieve promising results on a task that was deemed hard in previous nlp work, highlighting the importance of linguistic theory in nlp research. the gold standard corpus prepared for this task, containing 921 instances of one-anaphora marked in an easy-to-reuse standoff annotation scheme, will be released with this paper for future work.