Resolving Pronouns in T witter Streams: Context can Help! (2020)

Abstract: Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.

## introduction pronoun resolution is an important task in natural language processing @xcite @xcite . however, not a lot of work has been done to address pronoun resolution in twitter streams related to events. during a televised event such as a presidential debate or tv-shows, individuals publish tweets about people in the context in which they are being portrayed in the event @xcite . some of these tweets make reference to people using third-person singular pronouns like he, him, his, she, and her. for example, here are some tweets about an episode of the tv-show, game of thrones season 7 (gots7) that were published during the same minute while the episode was airing: (i) "she took his face", (ii) "walder frey?! probably just before arya killed him", and (iii) "wait where is arya did she change to his face". with short text such as these event-related tweets, although some tweets might mention the referents in the same tweets as the pronouns (e.g., in (ii) where "him" makes reference to the character "walder frey" in the same tweet), some other tweets may not contain the referents in the same tweets (e.g., in (i) and (iii), the pronouns {"she", "his"} and "his" respectively refer to the characters, {"arya", "walder frey"} and "walder frey" that are not mentioned in (i) and (iii) respectively). resolving a pronominal mention in an event-related tweet is a challenging task because a tweet with pronominal mentions either: (i) does not mention any person, (ii) makes reference to a person not mentioned in the tweet, and (iii) mentions more than one person (who may or may not have the same gender as the pronoun). in this paper, taking advantage of the context in which a pronoun is mentioned in the tweet, the tweet's temporal information, and the context in which other people are mentioned in tweets about the same event published at the same time period, we develop an algorithm to automatically resolve these third-person singular pronouns. we evaluate our algorithm on tweets collected around two events: (1) a united states (us) democratic party presidential debate and (2) an episode of a popular tv-show, gots7. we show that our algorithm outperforms baselines. we will make these datasets available to the research community. ## related work a lot of work on resolving pronouns in text has been done. in @xcite , a ranking approach for resolving pronouns in text was proposed. in @xcite , an entity-based coreference model which incrementally learns to resolve coreference was proposed. @xcite proposed a self-attention method to model zero pronouns. in @xcite , the eye movements of participants annotating documents were tracked to gain insights to some of the processes people use in coreference resolution. @xcite proposed an end-to-end coreference resolution model that is based on a neural model; the proposed model takes into consideration all the spans in a given text and given a span, it determines if there is a previous span which is an antecedent. @xcite proposed a coreference resolution model which uses fine-tuned bert embeddings. in @xcite , a large coreference resolution dataset was constructed. in @xcite , gender bias in coreference resolution was studied and it was shown that gender bias exists in some coreference resolution systems. in @xcite , a coreference resolution dataset focused on gender bias was constructed and similar to @xcite it was shown that some coreference resolution systems are gender biased; a model was proposed to remove these biases while maintaining the performance on coreference datasets. not a lot of prior work has been done to resolve pronouns in twitter data. the closest related work to our work is @xcite , which studies pronominal anaphora on conversations in twitter and constructs a corpus to determine relevant factors for resolving anaphora in twitter conversation data. our algorithm is different from @xcite because it focuses on resolving pronouns in tweets that make reference to people and characters portrayed in an event. ## dataset and labeling our dataset consist of tweets collected during the airing of an hour-long episode of the popular hbo show, gots7 and tweets collected during night 1 of the first 2020 us democratic party presidential debate (the first debate was held on 2 nights). ## our algorithm our algorithm has 3 steps: ## error analysis and future work in this work, we focused on gathering context in the form of tweets published in the same minute as the tweet with the pronomial mention. one of the challenges we observed is that in some cases a pronomial mention might make reference to a character or person not mentioned in the same minute, hence in the future, we plan to explore how far back in time we should expand this context prior. with regards to tweets with pronouns but no possible person referent, one future avenue to explore is to prepend these tweets with previous tweets published by the same authors and apply state-of-the-art coreference model on these expanded tweet "paragraphs". however, as we observe in our experiments, the last tweet prior may not contain the referent (as evident from the low recall of the "last person mention" baseline). therefore, we plan to explore how far back in tweet (or time) we should prepend these tweets. ## conclusion in this work, we develop an algorithm that resolves third-person singular pronouns in twitter data related to two events: a presidential debate and gots7. we show that our algorithm can help resolve thirdperson singular pronouns in these event-related tweets, even in cases where there are no possible person referent mentioned in a tweet. we also show that our method outperforms baselines.