The objective of Extreme Multi-Label Completion (XMLCo) is to predict missing document labels drawn from a very large collection. Together with Extreme Multi-Label Classification (XMLC), XMLCo is arguably one of the most challenging document classification tasks, as the number of potential labels is generally very large compared to the number of labeled documents. The collection of labels is often structured in a taxonomy that encodes relationships between labels, and many methods have been proposed to leverage this hierarchy to improve XMLCo algorithms. In this paper, we propose a new approach to this problem: TAMLEC (Taxonomy-Aware Multi-task Learning for Extreme multi-label Completion). TAMLEC divides the problem into several Taxonomy-Aware Tasks, i.e. into specific subsets of the labels drawn from paths in the taxonomy, and trains on these tasks using a dynamic Parallel Feature sharing approach where parts of the model are shared between tasks while others are task-specific. Then, at inference time, TAMLEC uses the labels available in a document to predict missing labels, using the Weak-Semilattice structure that is naturally induced by the tasks. Our empirical evaluation on real-world datasets shows that TAMLEC substantially outperforms the state-of-the-art in XMLCo. Furthermore, additional experiments show that TAMLEC is particularly suited for few-shot settings, where new tasks or labels are introduced with only few examples after initial training.

Extreme Multi-Label Classification (XMLC) plays a pivotal role in organizing and retrieving information in large-scale textual collections, by considering a very high number of potential labels for the documents. In this paper, we conduct an empirical evaluation of several XMLC approaches encompassing both dedicated techniques (AttentionXML and XR Transformer) and the use of Large Language Models (LLaMA2 7b Chat, LLaMA3 8b Instruct, and two Mistral models). We introduce both a new dataset based on OpenAlex as well as several new metrics to conduct our evaluations. Our results suggest that fine-tuning the LLMs using Low-Rank Adaptation significantly improves the performance of the models, bringing their results close to the ones of dedicated techniques. In the end, none of the method emerges as a clear winner, as picking the optimal XMLC technique heavily depends on the requirements of the use-case at hand.

Extreme Multi Label (XML) problems, and in particular XML completion -- the task of prediction the missing labels of an entity -- have attracted significant attention in the past few years. Most XML completion problems can organically leverage a label hierarchy, which can be represented as a tree that encodes the relations between the different labels.

Taxonomies are used to organize knowledge in many applications, including recommender systems, content browsing, or web search. With the emergence of new concepts, static taxonomies become obsolete as they fail to capture up-to-date knowledge. Several approaches have been proposed to address the problem of maintaining taxonomies automatically. These approaches typically rely on a limited set of neighbors to represent a given node in the taxonomy. However, considering distant nodes could improve the representation of some portions of the taxonomy, especially for those nodes situated in the periphery or in sparse regions of the taxonomy.

Finding companies' websites is important when building business databases. However, automatically finding a company's website based on its name or its official entry in a registry is challenging, as companies often have similar names, acronyms, or descriptions. In this context, we built a system to evaluate different features and classifiers to automatically identify a company's website from unstructured content.

Abstract In this brief communication, we evaluate the use of two stopword lists for the English language (one comprising 571 words and another with 9) and compare them with a search approach accounting for all word forms. We show that through implementing the original Okapi form or certain ones derived from the Divergence from Randomness (DFR) paradigm, significantly lower performance levels may result when using short or no stopword lists. For other DFR models and a revised Okapi implementation, performance differences between approaches using short or long stopword lists or no list at all are usually not statistically significant. Similar conclusions can be drawn when using other natural languages such as French, Hindi, or Persian.

Abstract This article describes and evaluates various information retrieval models used to search document collections written in English through submitting queries written in various other languages, either members of the Indo‐European family (English, French, German, and Spanish) or radically different language groups such as Chinese. This evaluation method involves searching a rather large number of topics (around 300) and using two commercial machine translation systems to translate across the language barriers. In this study, mean average precision is used to measure variances in retrieval effectiveness when a query language differs from the document language. Although performance differences are rather large for certain languages pairs, this does not mean that bilingual search methods are not commercially viable. Causes of the difficulties incurred when searching or during translation are analyzed and the results of concrete examples are explained.

Abstract This paper describes and evaluates various stemming and indexing strategies for the Russian language. We design and evaluate two stemming approaches, a light and a more aggressive one, and compare these stemmers to the Snowball stemmer, to no stemming, and also to a language‐independent approach (n‐gram). To evaluate the suggested stemming strategies we apply various probabilistic information retrieval (IR) models, including the Okapi, the Divergence from Randomness (DFR), a statistical language model (LM), as well as two vector‐space approaches, namely, the classical tf idf scheme and the dtu‐dtn model. We find that the vector‐space dtu‐dtn and the DFR models tend to result in better retrieval effectiveness than the Okapi, LM, or tf idf models, while only the latter two IR approaches result in statistically significant performance differences. Ignoring stemming generally reduces the MAP by more than 50%, and these differences are always significant. When applying an n‐gram approach, performance differences are usually lower than an approach involving stemming. Finally, our light stemmer tends to perform best, although performance differences between the light, aggressive, and Snowball stemmers are not statistically significant.