In written Chinese, personal pronouns are commonly dropped when they can be inferred from context. This practice is particularly common in informal genres like Short Message Service (SMS) messages sent via cell phones. Restoring dropped personal pronouns can be a useful preprocessing step for information extraction. Dropped personal pronoun recovery can be divided into two subtasks: (1) detecti...

Prescriptive and descriptive grammars of Hungarian frequently discuss variation in the choice of relative pronoun in Hungarian. This paper presents quantitative data about some long standing questions raised by prescriptivists, and a phenomenon that has not been explored: the lack of agreement between the pronoun and the antecedent in Colloquial Hungarian. The study presented here is based on t...

We present PROTEST, a test suite for the evaluation of pronoun translation by MT systems. The test suite comprises 250 hand-selected pronoun tokens and an automatic evaluation method which compares the translations of pronouns in MT output with those in the reference translation. Pronoun translations that do not match the reference are referred for manual evaluation. PROTEST is designed to supp...

In this paper, we present PYCOT, a pronoun resolution toolkit. This toolkit is written in the Python programming language and is intended to be an addition to the open-source NLTK collection of natural language processing tools. We discuss the design of the module as well as studies of its performance on pronoun resolution in English and in Korean.

Verbal communication is often ambiguous. By employing the event-related potential (ERP) technique, this study investigated how a comprehender resolves referential ambiguity by using information concerning the social status of communicators. Participants read a conversational scenario which included a minimal conversational context describing a speaker and two other persons of the same or differ...

We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.