Yuval Marom

An empirical study of corpus-based response automation methods for an e-mail-based help-desk domain

This article presents an investigation of corpus-based methods for the automation of help-desk e-mail responses. Specifically, we investigate this problem along two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. We consider two information-gathering techniques (retrieval and prediction) applied to information represented at two levels of granularity (document-level and sentence-level). Document-level methods correspond to the reuse of an existing response e-mail to address new requests. Sentence-level methods correspond to applying extractive multi-document summarization techniques to collate units of information from more than one e-mail. Evaluation of the performance of the different methods shows that in combination they are able to successfully automate the generation of responses for a substantial portion of e-mail requests in our corpus. We also investigate a meta-selection process that learns to choose one method to address a new inquiry e-mail, thus providing a unified response automation solution.

A comparative study of information-gathering approaches for answering help-desk email inquiries

We present a comparative study of corpus-based methods for the automatic synthesis of email responses to help-desk requests. Our methods were developed by considering two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. In particular, we investigate two techniques – retrieval and prediction – applied to information represented at two levels of granularity – sentence level and document level. We also developed a hybrid method that combines prediction with retrieval. Our results show that the different approaches are applicable in different situations, addressing a combined 72% of the requests with either complete or partial responses.

Analysis and synthesis of help-desk responses

We present a corpus-based approach for the automatic analysis and synthesis of email responses to help-desk requests. This approach can be used to automatically deal with repetitive requests of low technical content, thus enabling help-desk operators to focus their effort on more difficult requests. We propose a method for extracting high-precision sentences for inclusion in a response, and a measure for predicting the completeness of a planned response. The idea is that complete, high-precision responses may be sent directly to users, while incomplete responses should be passed to operators. Our results show that a small but significant proportion (14%) of our automatically generated responses have a high degree of precision and completeness, and that our measure can reliably predict the completeness of a response.

Automating Help-desk Responses: A Comparative Study of Information-gathering Approaches

We present a comparative study of corpus-based methods for the automatic synthesis of email responses to help-desk requests. Our methods were developed by considering two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. In particular, we investigate two techniques -- retrieval and prediction -- applied to information represented at two levels of granularity -- sentence-level and document level. We also developed a hybrid method that combines prediction with retrieval. Our results show that the different approaches are applicable in different situations, addressing a combined 72% of the requests with either complete or partial responses.

Filtering speaker-specific words from electronic discussions

The work presented in this paper is the first step in a project which aims to cluster and summarise electronic discussions in the context of help-desk applications. The eventual objective of this project is to use these summaries to assist help-desk users and operators. In this paper, we identify features of electronic discussions that influence the clustering process, and offer a filtering mechanism that removes undesirable influences. We tested the clustering and filtering processes on electronic newsgroup discussions, and evaluated their performance by means of two experiments: coarse-level clustering and simple information retrieval. Our evaluation shows that our filtering mechanism has a significant positive effect on both tasks.

Probabilistic, multi-staged interpretation of spoken utterances

We describe Scusi?, a multi-stage, spoken language interpretation mechanism designed to be part of a robot- mounted dialogue system. Scusi?s interpretation process maps spoken utterances to conceptual graphs, and the nodes in these graphs to concepts in the world. Maximum posterior probability is used to rank the (partial) interpretations produced at each stage of this process. We show how the features of our interpretation process yield desirable behaviours that support robust and flexible system performance.

Improving newsgroup clustering by filtering author-specific words

This paper describes the first step in a project for topic identification in help-desk applications. In this step, we apply a clustering mechanism to identify the topics of newsgroup discussions. We have used newsgroup discussions as our testbed, as they provide a good approximation to our target application, while obviating the need for manual tagging of topics. We have found that the postings of individuals who contribute repeatedly to a newsgroup may lead the clustering process astray, in the sense that discussions may be grouped according to their author, rather than according to their topic. To address this problem, we introduce a filtering mechanism, and evaluate it by comparing clustering performance with and without filtering.