Sujay Kumar Jauhar

Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models.

Words are polysemous. However, most approaches to representation learning for lexical semantics assign a single vector to every surface word type. Meanwhile, lexical ontologies such as WordNet provide a source of complementary knowledge to distributional information, including a word sense inventory. In this paper we propose two novel and general approaches for generating sense-specific word embeddings that are grounded in an ontology. The first applies graph smoothing as a postprocessing step to tease the vectors of different senses apart, and is applicable to any vector space model. The second adapts predictive maximum likelihood models that learn word embeddings with latent variables representing senses grounded in an specified ontology. Empirical results on lexical semantic tasks show that our approaches effectively captures information from both the ontology and distributional statistics. Moreover, in most cases our sense-specific models outperform other models we compare against.

Tables as Semi-structured Knowledge for Question Answering.

Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire. In this paper we explore tables as a semi-structured formalism that provides a balanced compromise to this trade-off. We first use the structure of tables to guide the construction of a dataset of over 9000 multiple-choice questions with rich alignment annotations, easily and efficiently via crowd-sourcing. We then use this annotated data to train a semi-structured feature-driven model for question answering that uses tables as a knowledge base. In benchmark evaluations, we significantly outperform both a strong unstructured retrieval baseline and a highly structured Markov Logic Network model.

Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It

Discourse deixis is a linguistic phenomenon in which pronouns have verbal or clausal, rather than nominal, antecedents. Studies have estimated that between 5% and 10% of pronouns in non-conversational data are discourse deictic. However, current coreference resolution systems ignore this phenomenon. This paper presents an automatic system for the detection and resolution of discourse-deictic pronouns. We introduce a two-step approach that first recognizes instances of discourse-deictic pronouns, and then resolves them to their verbal antecedent. Both components rely on linguistically motivated features. We evaluate the components in isolation and in combination with two state-of-the-art coreference resolvers. Results show that our system outperforms several baselines, including the only comparable discourse deixis system, and leads to small but statistically significant improvements over the full coreference resolution systems. An error analysis lays bare the need for a less strict evaluation of this task.