Oren Glickman

Direct Word Sense Matching for Lexical Substitution

This paper investigates conceptually and empirically the novel sense matching task, which requires to recognize whether the senses of two synonymous words match in context. We suggest direct approaches to the problem, which avoid the intermediate step of explicit word sense disambiguation, and demonstrate their appealing advantages and stimulating potential for future research.

Definition and Analysis of Intermediate Entailment Levels

In this paper we define two intermediate models of textual entailment, which correspond to lexical and lexical-syntactic levels of representation. We manually annotated a sample from the RTE dataset according to each model, compared the outcome for the two models, and explored how well they approximate the notion of entailment. We show that the lexical-syntactic model outperforms the lexical model, mainly due to a much lower rate of false-positives, but both models fail to achieve high recall. Our analysis also shows that paraphrases stand out as a dominant contributor to the entailment task. We suggest that our models and annotation methods can serve as an evaluation scheme for entailment at these levels.

A Probabilistic Setting and Lexical Coocurrence Model for Textual Entailment

This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approaches for entailment modeling.

Lexical Reference: a Semantic Matching Subtask

Semantic lexical matching is a prominent subtask within text understanding applications. Yet, it is rarely evaluated in a direct manner. This paper proposes a definition for lexical reference which captures the common goals of lexical matching. Based on this definition we created and analyzed a test dataset that was utilized to directly evaluate, compare and improve lexical matching models. We suggest that such decomposition of the global semantic matching task is critical in order to fully understand and improve individual components.

Investigating Lexical Substitution Scoring for Subtitle Generation

This paper investigates an isolated setting of the lexical substitution task of replacing words with their synonyms. In particular, we examine this problem in the setting of subtitle generation and evaluate state of the art scoring methods that predict the validity of a given substitution. The paper evaluates two context independent models and two contextual models. The major findings suggest that distributional similarity provides a useful complementary estimate for the likelihood that two Wordnet synonyms are indeed substitutable, while proper modeling of contextual constraints is still a challenging task for future research.

A lexical alignment model for probabilistic textual entailment

This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis.