Assigning Function Tags with a Simple Model

Int. J. Artif. Intell. Tools

2008

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This paper describes the use of two machine learning techniques, naive Bayes and decision trees, to address the task of assigning function tags to nodes in a syntactic parse tree. Function tags are extra functional information, such as logical subject or predicate, that can be added to certain nodes in syntactic parse trees. We model the function tags assignment problem as a classification problem. Each function tag is regarded as a class and the task is to find what class/tag a given node in a parse tree belongs to from a set of predefined classes/tags. The paper offers the first systematic comparison of the two techniques, naive Bayes and decision trees, for the task of function tags assignment. The comparison is based on a standardized data set, the Penn Treebank, a collection of sentences annotated with syntactic information including function tags. We found out that decision trees generally outperform naive Bayes for the task of function tagging. Furthermore, this is the first large scale evaluation of decision trees based solutions to the task of functional tagging.

“…In Rus and Desai, 19 preliminary work on function tagging with a naive-Bayes approach is reported. The major difference from our work is that they used different data preprocessing steps.…”

Section: Related Workmentioning

confidence: 99%

Large Scale Experiments With Naive Bayes and Decision Trees for Function Tagging

Lintean

Int. J. Artif. Intell. Tools

2008

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Identification of Sentence-to-Sentence Relations Using a Textual Entailer

McCarthy

Graesser

et al. 2009

Res on Lang and Comput

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We show in this article how an approach developed for the task of recognizing textual entailment relations can be extended to identify paraphrase and elaboration relations. Entailment is a unidirectional relation between two sentences in which one sentence logically infers the other. There seems to be a close relation between entailment and two other sentence-to-sentence relations: elaboration and paraphrase. This close relation is discussed to theoretically justify the newly derived approaches. The proposed approaches use lexical, syntactic, and shallow negation handling. The proposed approaches offer significantly better results than several baselines. When compared to other paraphrase and elaboration approaches they produce similar or better results. We report results on several data sets: the Microsoft Research Paraphrase corpus, a benchmark for evaluating approaches to paraphrase identification, and a data set collected from high-school students' interactions with an intelligent tutoring system iSTART, which includes both paraphrase and elaboration utterances.

A study on textual entailment

17th IEEE International Conference on Tools With Artificial Intelligence (ICTAI'05)

Graesser

McCarthy

et al. 2005

This paper studies the role of dependency information for the task of textual entailment. Both the Text and Hypothesis of an entailment pair are mapped into sets of dependencies and a score is computed that measures the similarity of the two sets. Based on the score an entailment decision is made. Two experiments are conducted to measure the impact of dependencies on the entailment task. In one experiment we compare the dependency-based approach with a baseline approach on a standard data set. In a second experiment, we measure the performance on a subset of the standard data set. The subset is so selected to minimize the effect of other factors, such as word-level information, in the performance measurement process. A brief discussion compares the dependency-based approach to other, similar approaches.