Building a 70 billion word corpus of English from ClueWeb

This work describes the process of creation of a 70 billion word text corpus of English. We used an existing language resource, namely the ClueWeb09 dataset, as source for the corpus data. Processing such a vast amount of data presented several challenges, mainly associated with pre-processing (boilerplate cleaning, text de-duplication) and post-processing (indexing for efficient corpus querying using the CQL – Corpus Query Language) steps. In this paper we explain how we tackled them: we describe the tools used for boilerplate cleaning (jusText) and for de-duplication (onion) that was performed not only on full (document-level) duplicates but also on the level of near-duplicate texts. Moreover we show the impact of each of the performed pre-processing steps on the final corpus size. Furthermore we show how effective parallelization of the corpus indexation procedure was employed within the Manatee corpus management system and during computation of word sketches (one-page, automatic, corpus-derived summaries of a word’s grammatical and collocational behaviour) from the resulting corpus

Harvesting Entities from the Web Using Unique Identifiers -- IBEX

In this paper we study the prevalence of unique entity identifiers on the Web. These are, e.g., ISBNs (for books), GTINs (for commercial products), DOIs (for documents), email addresses, and others. We show how these identifiers can be harvested systematically from Web pages, and how they can be associated with human-readable names for the entities at large scale. Starting with a simple extraction of identifiers and names from Web pages, we show how we can use the properties of unique identifiers to filter out noise and clean up the extraction result on the entire corpus. The end result is a database of millions of uniquely identified entities of different types, with an accuracy of 73--96% and a very high coverage compared to existing knowledge bases. We use this database to compute novel statistics on the presence of products, people, and other entities on the Web.Comment: 30 pages, 5 figures, 9 tables. Complete technical report for A. Talaika, J. A. Biega, A. Amarilli, and F. M. Suchanek. IBEX: Harvesting Entities from the Web Using Unique Identifiers. WebDB workshop, 201

Large-Scale information extraction from textual definitions through deep syntactic and semantic analysis

We present DEFIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations

Retrieval Enhancements for Task-Based Web Search

The task-based view of web search implies that retrieval should take the user perspective into account. Going beyond merely retrieving the most relevant result set for the current query, the retrieval system should aim to surface results that are actually useful to the task that motivated the query. This dissertation explores how retrieval systems can better understand and support their users’ tasks from three main angles: First, we study and quantify search engine user behavior during complex writing tasks, and how task success and behavior are associated in such settings. Second, we investigate search engine queries formulated as questions, and explore patterns in a large query log that may help search engines to better support this increasingly prevalent interaction pattern. Third, we propose a novel approach to reranking the search result lists produced by web search engines, taking into account retrieval axioms that formally specify properties of a good ranking.Die Task-basierte Sicht auf Websuche impliziert, dass die Benutzerperspektive berücksichtigt werden sollte. Über das bloße Abrufen der relevantesten Ergebnismenge für die aktuelle Anfrage hinaus, sollten Suchmaschinen Ergebnisse liefern, die tatsächlich für die Aufgabe (Task) nützlich sind, die diese Anfrage motiviert hat. Diese Dissertation untersucht, wie Retrieval-Systeme die Aufgaben ihrer Benutzer besser verstehen und unterstützen können, und leistet Forschungsbeiträge unter drei Hauptaspekten: Erstens untersuchen und quantifizieren wir das Verhalten von Suchmaschinenbenutzern während komplexer Schreibaufgaben, und wie Aufgabenerfolg und Verhalten in solchen Situationen zusammenhängen. Zweitens untersuchen wir Suchmaschinenanfragen, die als Fragen formuliert sind, und untersuchen ein Suchmaschinenlog mit fast einer Milliarde solcher Anfragen auf Muster, die Suchmaschinen dabei helfen können, diesen zunehmend verbreiteten Anfragentyp besser zu unterstützen. Drittens schlagen wir einen neuen Ansatz vor, um die von Web-Suchmaschinen erstellten Suchergebnislisten neu zu sortieren, wobei Retrieval-Axiome berücksichtigt werden, die die Eigenschaften eines guten Rankings formal beschreiben

Neural Models for Information Retrieval without Labeled Data

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or \emph{weakly supervised} solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification. We further propose a standalone learning to rank model based on deep neural networks. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available

Vacaspati: A Diverse Corpus of Bangla Literature

Bangla (or Bengali) is the fifth most spoken language globally; yet, the state-of-the-art NLP in Bangla is lagging for even simple tasks such as lemmatization, POS tagging, etc. This is partly due to lack of a varied quality corpus. To alleviate this need, we build Vacaspati, a diverse corpus of Bangla literature. The literary works are collected from various websites; only those works that are publicly available without copyright violations or restrictions are collected. We believe that published literature captures the features of a language much better than newspapers, blogs or social media posts which tend to follow only a certain literary pattern and, therefore, miss out on language variety. Our corpus Vacaspati is varied from multiple aspects, including type of composition, topic, author, time, space, etc. It contains more than 11 million sentences and 115 million words. We also built a word embedding model, Vac-FT, using FastText from Vacaspati as well as trained an Electra model, Vac-BERT, using the corpus. Vac-BERT has far fewer parameters and requires only a fraction of resources compared to other state-of-the-art transformer models and yet performs either better or similar on various downstream tasks. On multiple downstream tasks, Vac-FT outperforms other FastText-based models. We also demonstrate the efficacy of Vacaspati as a corpus by showing that similar models built from other corpora are not as effective. The models are available at https://bangla.iitk.ac.in/

Indexing Proximity-based Dependencies for Information Retrieval

Research into term dependencies for information retrieval has demonstrated that dependency retrieval models are able to consistently improve retrieval effectiveness over bag-of-words models. However, the computation of term dependency statistics is a major efficiency bottleneck in the execution of these retrieval models. This thesis investigates the problem of improving the efficiency of dependency retrieval models without compromising the effectiveness benefits of the term dependency features. Despite the large number of published comparisons between dependency models and bag-of-words approaches, there has been a lack of direct comparisons between alternate dependency models. We provide this comparison and investigate different types of proximity features. Several bi-term and many-term dependency models over a range of TREC collections, for both short (title) and long (description) queries, are compared to determine the strongest benchmark models. We observe that the weighted sequential dependence model is the most effective model studied. Additionally, we observe that there is some potential in many-term dependencies, but more selective methods are required to exploit these features. We then investigate two novel index structures to directly index the proximitybased dependencies used in the sequential dependence model and weighted sequential dependence model. The frequent index and the sketch index data structures can both provide efficient access to collection and document level statistics for all indexed term dependencies, while minimizing space costs, relative to a full inverted index of term dependencies. We test whether these structures can improve retrieval efficiency without incurring large space requirements, or degrading retrieval effectiveness significantly. A secondary requirement is that each data structure must be able to be constructed for an input text collection in a scalable and distributed manner. Based on the observation that the vast majority of term dependencies extracted from queries are relatively frequent in the collection, the “frequent” index of term dependencies omits data for infrequent term dependencies. The sketch index of term dependencies uses techniques from sketch data structures to store probabilisticallybounded estimates of the required statistics. We present analyses of these data structures that include construction and space costs, retrieval efficiency and investigation of any degradation of retrieval effectiveness. Finally, we investigate the application of these data structures to the execution of the strongest performing dependency models identified. We compare the retrieval efficiency of each of these structures across two query processing algorithms, and across both short and long queries, using two large web collections. We observe that these newly proposed data structures allow the execution of queries considerably faster than when using positional indexes, and as fast as a full index of term dependencies, but with lowered storage overhead

Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task

Large Language Models (LLMs) are increasingly bringing advances to Natural Language Processing. However, low-resource languages, those lacking extensive prominence in datasets for various NLP tasks, or where existing datasets are not as substantial, such as Portuguese, already obtain several benefits from LLMs, but not to the same extent. LLMs trained on multilingual datasets normally struggle to respond to prompts in Portuguese satisfactorily, presenting, for example, code switching in their responses. This work proposes a fine-tuned LLaMA 2-based model for Portuguese prompts named Bode in two versions: 7B and 13B. We evaluate the performance of this model in classification tasks using the zero-shot approach with in-context learning, and compare it with other LLMs. Our main contribution is to bring an LLM with satisfactory results in the Portuguese language, as well as to provide a model that is free for research or commercial purposes.Comment: 10 pages, 3 figure