ABSTRACT Natural language is a key form of business communication. Textual analysis the application natural processing (NLP) to textual data for automated information extraction or measurement. We survey publications in top accounting journals and describe trend current state accounting. organize available NLP methods unified framework. Accounting researchers have often used measure disclosure ...

This paper presents novel methods for modeling numerical common sense: the ability to infer whether a given number (e.g., three billion) is large, small, or normal for a given context (e.g., number of people facing a water shortage). We first discuss the necessity of numerical common sense in solving textual entailment problems. We explore two approaches for acquiring numerical common sense. Bo...

This paper describes our participation in the Recognizing Textual Entailment challenge (RTE-5) in the Text Analysis Conference (TAC 2009). Following the two-stage binary classification strategy, our focus this year is to recognize related Text-Hypothesis pairs instead of entailment pairs. In particular, we propose a joint syntactic-semantic representation to better capture the key information s...

This paper presents a machine learning system that uses dependency-based features and lexical features for recognizing textual entailment. The proposed system evaluates the feature values automatically. The performance of the proposed system is evaluated by conducting experiments on RTE1, RTE2 and RTE3 datasets. Further, a comparative study of the current system with other ML-based systems for ...

In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aim...

Existing evaluation metrics for machine translation lack crucial robustness: their correlations with human quality judgments vary considerably across languages and genres. We believe that the main reason is their inability to properly capture meaning: A good translation candidate means the same thing as the reference translation, regardless of formulation. We propose a metric that evaluates MT ...

This paper discusses our contribution to the third RTE Challenge – the SALSA RTE system. It builds on an earlier system based on a relatively deep linguistic analysis, which we complement with a shallow component based on word overlap. We evaluate their (combined) performance on various data sets. However, earlier observations that the combination of features improves the overall accuracy could...