The cross-lingual pronoun prediction task at WMT 2016 requires to restore the missing target pronouns from source text and target lemmatized and POS-tagged translations. We study the benefits for this task of a specific Pronoun Language Model (PLM), which captures the likelihood of a pronoun given the gender and number of the nouns or pronouns preceding it, on the target-side only. Experimentin...

This paper discusses a corpus-based investigation of the distribution of the thirdperson neuter singular pronoun in Dutch (“het”). We labeled all pronominal occurrences of “het” in a large corpus of documents. On the basis of the annotated corpora, we developed an automatic classification system using machine learning techniques to distinguish between the different uses of the neuter pronoun. A...

This paper presents a solution for resolving inter-sentential anaphoric pronoun “nó” in Vietnamese paragraphs composing 3 to 5 simple sentences. In Vietnamese, “nó” is a special pronoun, can be used to indicate human, animal or non-animate object depending on the content and context of the paragraph (so in English, we should use “he”, “she” or “it”) . The proposed solution consists of some find...

State-of-the-art Chinese zero pronoun resolution systems are supervised, thus relying on training data containing manually resolved zero pronouns. To eliminate the reliance on annotated data, we present a generative model for unsupervised Chinese zero pronoun resolution. At the core of our model is a novel hypothesis: a probabilistic pronoun resolver trained on overt pronouns in an unsupervised...

We describe three experiments that use priming methodology to investigate the nature of the abstract mental representations activated during pronoun resolution, in order to contribute to our understanding of how structural representations and semantic coherence representations influence pronoun interpretation. The results of Experiment 1 suggest that there exists a level of abstract anaphoric d...

A recently-proposed machine learning approach to reference resolution — the twin-candidate approach — has been shown to be more promising than the traditional single-candidate approach. This paper presents a pronoun interpretation system that extends the twin-candidate framework by (1) equipping it with the ability to identify non-referential pronouns, (2) training different models for handling...

We describe the DARE corpus, an annotated data set focusing on pronoun resolution in tutorial dialogue. Although data sets for general purpose anaphora resolution exist, they are not suitable for dialogue based Intelligent Tutoring Systems. To the best of our knowledge, no data set is currently available for pronoun resolution in dialogue based intelligent tutoring systems. The described DARE c...

We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic corefe...