Nanda Kambhatla

Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations

Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results.

A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree

This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space and casts the coreference resolution problem as finding the best path from the root of the Bell tree to the leaf nodes. A Maximum Entropy model is used to rank these paths. The coreference performance on the 2002 and 2003 Automatic Content Extraction (ACE) data will be reported. We also train a coreference system using the MUC6 data and competitive results are obtained.

tRuEcasIng

Truecasing is the process of restoring case information to badly-cased or noncased text. This paper explores truecasing issues and proposes a statistical, language modeling based truecaser which achieves an accuracy of ∼98% on news articles. Task based evaluation shows a 26% F-measure improvement in named entity recognition when using truecasing. In the context of automatic content extraction, mention detection on automatic speech recognition text is also improved by a factor of 8. Truecasing also enhances machine translation output legibility and yields a BLEU score improvement of 80.2%. This paper argues for the use of truecasing as a valuable component in text processing applications.

Natural Language Assistant: A Dialog System for Online Product Recommendation

With the emergence of electronic-commerce systems, successful information access on electroniccommerce web sites becomes essential. Menu-driven navigation and keyword search currently provided by most commercial sites have considerable limitations because they tend to overwhelm and frustrate users with lengthy, rigid, and ineffective interactions. To provide an efficient solution for information access, we have built the NATURAL language ASSISTANT (NLA), a web-based natural language dialog system to help users find relevant products on electronic-commerce sites. The system brings together technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with web sites. By combining statistical parsing techniques with traditional AI rule-based technology, we have created a dialog system that accommodates both customer needs and business requirements. The system is currently embedded in an application for recommending laptops and was deployed as a pilot on IBMs web site.

Natural language dialogue for personalized interaction

T he pragmatic goal of natural language (NL) and multimodal interfaces (speech recognition, keyboard entry, pointing, among others) is to enable ease-of-use for users/customers in performing more sophisticated human-computer interactions (HCI). NL research attempts to define extensive discourse models that in turn provide improved models of context-enabling HCI and personalization. Customers have the initiative to Technologies that successfully recognize and react to spoken or typed words are key to true personalization. Front-and back-end systems must respond in accord, and one solution may be found somewhere in the middle(ware).

A Flexible Framework for Developing Mixed-Initiative Dialog Systems

We present a new framework for rapid development of mixed-initiative dialog systems. Using this framework, a developer can author sophisticated dialog systems for multiple channels of interaction by specifying an interaction modality, a rich task hierarchy and task parameters, and domain-specific modules. The framework includes a dialog history that tracks input, output, and results. We present the framework and preliminary results in two application domains.

Minority Vote: At-Least-N Voting Improves Recall for Extracting Relations

Several NLP tasks are characterized by asymmetric data where one class label NONE, signifying the absence of any structure (named entity, coreference, relation, etc.) dominates all other classes. Classifiers built on such data typically have a higher precision and a lower recall and tend to overproduce the NONE class. We present a novel scheme for voting among a committee of classifiers that can significantly boost the recall in such situations. We demonstrate results showing up to a 16% relative improvement in ACE value for the 2004 ACE relation extraction task for English, Arabic and Chinese.

Identifying and tracking entity mentions in a maximum entropy framework

We present a system for identifying and tracking named, nominal, and pronominal mentions of entities within a text document. Our maximum entropy model for mention detection combines two pre-existing named entity taggers (built to extract different entity categories) and other syntactic and morphological feature streams to achieve competitive performance. We developed a novel maximum entropy model for tracking all mentions of an entity within a document. We participated in the Automatic Content Extraction (ACE) evaluation and performed well. We describe our system and present results of the ACE evaluation.

The Role of a Natural Language Conversational Interface in Online Sales: A Case Study

This paper describes the evaluation of a natural language dialog-based navigation system (HappyAssistant) that helps users access e-commerce sites to find relevant information about products and services. The prototype system leverages technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with websites, especially for less experienced users. The result of a comparative study shows that users prefer the natural language-enabled navigation two to one over the menu driven navigation. In addition, the study confirmed the efficiency of using natural language dialog in terms of the number of clicks and the amount of time required to obtain the relevant information. In the case study, as compared to the menu driven system, the average number of clicks used in the natural language system was reduced by 63.2% and the average time was reduced by 33.3%.

A conversational interface for online shopping

We present a deployed, conversational dialog system that assists users in finding computers based on their usage patterns and constraints on specifications. We discuss findings from a market survey and two user studies. We compared our system to a directed dialog system and a menu driven navigation system. We found that the conversational interface reduced the average number of clicks by 63% and the average interaction time by 33% over a menu driven search system. The focus of our continuing work includes developing a dynamic, adaptive dialog management strategy, robustly handling user input and improving the user interface.