Lisa Ballesteros

Phrasal translation and query expansion techniques for cross-language information retrieval

Dictionary methods for cross-language information retrieval give performance below that for mono-lingual retrieval. Failure to translate multi-term phrases has been shown to be one of the factors responsible for the errors associated with dictionary methods. First, we study the importance of phrasal translation for this approach. Second, we explore the role of phrases in query expansion via local context analysis and local feedback and show how they can be used to significantly reduce the error associated with automatic dictionary translation.

Resolving ambiguity for cross-language retrieval

One of the main hurdles to improved CLIR effectiveness is resolving ambiguity associated with translation. Availability of resources is also a problem. First we present a technique based on co-occurrence statistics from unlinked corpora which can be used to reduce the ambiguity associated with phrasal and term translation. We then combine this method with other techniques for reducing ambiguity and achieve more than 90% monolingual effectiveness. Finally, we compare the co-occurrence method with parallel corpus and machine translation techniques and show that good retrieval effectiveness can be achieved without complex resources.

Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis

Arabic, a highly inflected language, requires good stemming for effective information retrieval, yet no standard approach to stem¿ming has emerged. We developed several light stemmers based on heuristics and a statistical stemmer based on co-occurrence for Arabic retrieval. We compared the retrieval effectiveness of our stemmers and of a morphological analyzer on the TREC-2001 data. The best light stemmer was more effective for cross-lan¿guage retrieval than a morphological stemmer which tried to find the root for each word. A repartitioning process consisting of vowel removal followed by clustering using co-occurrence analy¿sis pro¿duced stem classes which were better than no stemming or very light stemming, but still inferior to good light stemming or mor¿phological analysis.

Light Stemming for Arabic Information Retrieval

Computational Morphology is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. We have found, however, that a full solution to this problem is not required for effective information retrieval. Light stemming allows remarkably good information retrieval without providing correct morphological analyses. We developed several light stemmers for Arabic, and assessed their effectiveness for information retrieval using standard TREC data. We have also compared light stemming with several stemmers based on morphological analysis. The light stemmer, light10, outperformed the other approaches. It has been included in the Lemur toolkit, and is becoming widely used Arabic information retrieval.

Dictionary Methods for Cross-Lingual Information Retrieval

Multi-lingual information retrieval (IR) has largely been limited to the development of systems for use with a specific foreign language. The explosion in the availability of electronic media in languages other than English makes the development of IR systems that can cross language boundaries increasingly important. In this paper, we present experiments that analyze the factors that affect dictionary based methods for cross-lingual retrieval and present methods that dramatically reduce the errors such an approach usually makes.

Cross-Language Retrieval via Transitive Translation

The growth in availability of multi-lingual data in all areas of the public and private sector is driving an increasing need for systems that facilitate access to multi-lingual resources. Cross-language Retrieval (CLR) technology is a means of addressing this need.

Statistical Methods for Cross-Language Information Retrieval

Multi-lingual information retrieval (IR) has largely been limited to the development of multiple systems for use with a specific foreign language. The explosion in the availability of electronic media in languages other than English makes the development of IR systems that can cross language boundaries increasingly important. We are currently developing tools and techniques for Cross Language Information Retrieval. In this chapter, we present experiments that analyze the factors that affect dictionary based methods for cross-language retrieval and present methods that dramatically reduce the errors such an approach usually makes.

Addressing the lack of direct translation resources for cross-language retrieval

Most cross language information retrieval research concentrates on language pairs for which direct, rich, and often multiple translation resources already exist. However, for most language pairs, translation via an intermediate language is necessary. Two distinct methods for dealing with the additional ambiguity introduced by the extra translation step have been proposed and individually, shown to improve retrieval effectiveness. Two previous works indicated that in combination, the methods were ineffective. This paper provides strong empirical evidence that the methods can be combined to produce consistent and often significant improvements in retrieval effectiveness. The improvement is shown across a number of different intermediate languages and test collections.

Path Analysis Models Of An Autonomous Agent In A Complex Environment

We seek explanatory models of how and why AI systems work in particular environments. We are not satisfied to demonstrate performance, we want to understand it. In terms of data and models, this means we are not satisfied with descriptive summaries, nor even with predictive models. We want causal models. In this brief abstract we will present descriptive, predictive and causal models of the behavior of agents that fight simulated forest fires. We will describe the shortcomings of descriptive and predictive models, and summarize path analysis, a common technique for inducing causal models.

A clustered retrieval approach for categorizing and annotating images

Images are difficult to classify and annotate but the availability of digital image databases creates a constant demand for tools that automatically analyze image content and describe it with either a category or set of words. We develop two cluster-based cross-media relevance models that effectively categorize and annotate images by adapting a cross-lingual retrieval technique to choose the terms most likely associated with the visual features of an image.