T wi C onv: A Coreference-annotated Corpus of T witter Conversations (2020)

Abstract: This article introduces TwiConv, an English coreference-annotated corpus of microblog conversations from Twitter. We describe the corpus compilation process and the annotation scheme, and release the corpus publicly, along with this paper. We manually annotated nominal coreference in 1756 tweets arranged in 185 conversation threads. The annotation achieves satisfactory annotation agreement results. We also present a new method for mapping the tweet contents with distributed stand-off annotations, which can easily be adapted to different annotation tasks.

## introduction and related work microblog texts from twitter present a discourse genre that carries non-standard language characteristics (e.g., noisy or informal language with abbreviations, purposeful typos, use of non-alphanumerical symbols such as #-and @-characters, misspellings, etc.) and is therefore challenging for nlp applications @xcite . there exist a number of twitter datasets annotated at different linguistic layers for investigating a variety of nlp tasks on this genre, including sentiment analysis @xcite , named entity recognition @xcite , and event coreference resolution @xcite . @xcite tested an out-of-the-box nominal coreference resolution system trained on ontonotes @xcite on twitter data and showed that the system performs with much lower scores than the original reported values on that data. hence, tweets are a complicated genre also for the task of nominal coreference resolution. we introduce twiconv, a nominal coreference-annotated corpus of english-language twitter posts with the intent to explore the coreference features in conversational twitter texts. our annotation scheme is based on @xcite , yet with some domain-driven adaptations. twitter's developer policy 1 does not allow publishing the tweet contents. therefore, most of the tweet datasets distribute the unique tweet ids and annotations without the tweet text. however, if the tokenization of the corpus in concern is realized through a relatively complicated procedure or contains manual corrections, stand off annotation layers may not match with the text content in the compiled corpus. we thus present a distribution method for mapping the original tweet texts with our annotations. to our knowledge, twiconv is the first tweet corpus for nominal coreference. the remainder of paper is organized as follows. we describe the corpus compilation process in section 2. in section 3, we present the annotation principles along with a description of quality assurance methods. the main statistics of our corpus are presented in section 4. format of the distributed corpus and data sharing methodology are described in section 5. section 6 summarizes the presented work. ## corpus overview the resulting twiconv corpus consists of 1756 tweets in 185 threads, with the average length of a tweet being 153 characters. we present additional descriptive statistics for twiconv corpus in ## conclusion we have developed a comprehensive annotation scheme for annotating nominal coreference in english twitter conversations and fully annotated 1756 tweets arranged in 185 threads. assessment of annotations and correction of erroneous cases were made via inter-annotator agreement evaluation, partial review, and automated checks. we distribute the corpus without tweet contents and introduce tools for researchers to map the tweet texts, captured using the tweet ids, with the shared annotations. we hope that the release of the twiconv corpus will increase the interest in coreference studies on this genre.