Boilerplate Removal using a Neural Sequence Labeling Model

The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.Comment: WWW20 Demo pape

GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training

Large pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly parse webpages. Consequently, we believe that common machine learning tasks like content extraction and information mining from webpages have low-hanging gains that yet remain untapped. We aim to close the gap by introducing an agnostic deep graph neural network feature extractor that can ingest webpage structures, pre-train self-supervised on massive unlabeled data, and fine-tune to arbitrary tasks on webpages effectually. Finally, we show that our pre-trained model achieves state-of-the-art results using multiple datasets on two very different benchmarks: webpage boilerplate removal and genre classification, thus lending support to its potential application in diverse downstream tasks.Comment: Submitted to CIKM '2

Distinguishing Noise and Main Text Content from Web-Sourced Plain Text Documents Using Sequential Neural Networks

Boilerplate removal and the identification of the actual textual content is a crucial step in web corpus creation. However, existing methods don’t always filter out the noise perfectly and are often not applicable for plain text corpora. In this thesis, I will develop machine learning methods to identify the main textual content in plain text documents. I will utilize transfer learning and pretrained language models as a base for training monolingual models with French and Swedish data as well as a multilingual model with French, Swedish, English, Finnish, German and Spanish data. I will compare two machine learning architectures based on the XLM-RoBERTa language model: first a classification model built on top of the pretrained XLM-RoBERTa model and a second model using an additional Long Short-Term Memory (LSTM) network layer. I will show that the LSTM layer improves the classification of the XLM-RoBERTa model and the built multilingual model performs well even with data in unseen languages. I will perform a further analysis on the results and show that the results of the boilerplate detection with the trained models differ with text varieties. Certain types of text documents, such as lyrical texts or discussion forum texts pose challenges in boilerplate detection, and it would be beneficial for future research to focus on gathering data that has been difficult to clean

An unsupervised perplexity-based method for boilerplate removal

The availability of large web-based corpora has led to significant advances in a wide range of technologies, including massive retrieval systems or deep neural networks. However, leveraging this data is challenging, since web content is plagued by the so-called boilerplate: ads, incomplete or noisy text and rests of the navigation structure, such as menus or navigation bars. In this work, we present a novel and efficient approach to extract useful and well-formed content from web-scraped data. Our approach takes advantage of Language Models and their implicit knowledge about correctly formed text, and we demonstrate here that perplexity is a valuable artefact that can contribute in terms of effectiveness and efficiency. As a matter of fact, the removal of noisy parts leads to lighter AI or search solutions that are effective and entail important reductions in resources spent. We exemplify here the usefulness of our method with two downstream tasks, search and classification, and a cleaning task. We also provide a Python package with pre-trained models and a web demo demonstrating the capabilities of our approachS

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition

Efficient and Explainable Neural Ranking

The recent availability of increasingly powerful hardware has caused a shift from traditional information retrieval (IR) approaches based on term matching, which remained the state of the art for several decades, to large pre-trained neural language models. These neural rankers achieve substantial improvements in performance, as their complexity and extensive pre-training give them the ability of understanding natural language in a way. As a result, neural rankers go beyond term matching by performing relevance estimation based on the semantics of queries and documents. However, these improvements in performance don't come without sacrifice. In this thesis, we focus on two fundamental challenges of neural ranking models, specifically, ones based on large language models: On the one hand, due to their complexity, the models are inefficient; they require considerable amounts of computational power, which often comes in the form of specialized hardware, such as GPUs or TPUs. Consequently, the carbon footprint is an increasingly important aspect of systems using neural IR. This effect is amplified when low latency is required, as in, for example, web search. On the other hand, neural models are known for being inherently unexplainable; in other words, it is often not comprehensible for humans why a neural model produced a specific output. In general, explainability is deemed important in order to identify undesired behavior, such as bias. We tackle the efficiency challenge of neural rankers by proposing Fast-Forward indexes, which are simple vector forward indexes that heavily utilize pre-computation techniques. Our approach substantially reduces the computational load during query processing, enabling efficient ranking solely on CPUs without requiring hardware acceleration. Furthermore, we introduce BERT-DMN to show that the training efficiency of neural rankers can be improved by training only parts of the model. In order to improve the explainability of neural ranking, we propose the Select-and-Rank paradigm to make ranking models explainable by design: First, a query-dependent subset of the input document is extracted to serve as an explanation; second, the ranking model makes its decision based only on the extracted subset, rather than the complete document. We show that our models exhibit performance similar to models that are not explainable by design and conduct a user study to determine the faithfulness of the explanations. Finally, we introduce BoilerNet, a web content extraction technique that allows the removal of boilerplate from web pages, leaving only the main content in plain text. Our method requires no feature engineering and can be used to aid in the process of creating new document corpora from the web

Technologies for extracting and analysing the credibility of health-related online content

The evolution of the Web has led to an improvement in information accessibility. This change has allowed access to more varied content at greater speed, but we must also be aware of the dangers involved. The results offered may be unreliable, inadequate, or of poor quality, leading to misinformation. This can have a greater or lesser impact depending on the domain, but is particularly sensitive when it comes to health-related content. In this thesis, we focus in the development of methods to automatically assess credibility. We also studied the reliability of the new Large Language Models (LLMs) to answer health questions. Finally, we also present a set of tools that might help in the massive analysis of web textual content