Languages do not exist in isolation. Languages coexist; speakers of different languages interact. Contact between languages is one of the major inducers of language change. Speakers may take words that belong to one language and incorporate them into another language, a process that is known as lexical borrowing. As lexical borrowings are a source of new words, the automatic identification of borrowings has been shown to be useful both for Linguistics (to assist data-driven research in contact linguistics, historical linguistics and lexicography) and in Natural Language Processing (as a pre-processing step for NLP downstream tasks, or when working on low-resource settings). However, previous work on lexical borrowing within Linguistics has mainly relied on manual inspection of corpora, an approach that is insufficient to account for an on-going and sparse phenomenon like borrowing incorporation. On the other hand, research within NLP has usually framed lexical borrowing identification as a word classification problem, in which isolated words deprived from context are classified as being a borrowing or not. This approach ignores the fact that context is key when deciding whether a given word is a borrowing or not, and is insufficient to account for multiword borrowings, adjacency or contextual ambiguity, which are all prevalent phenomena in real-world data. This dissertation aims to provide computer-assisted methods for automatically retrieving lexical borrowings from text, with a focus on the identification of English lexical borrowings (or anglicisms) in Spanish. Contrary to previous work, we propose to frame lexical borrowing identification as a sequence labeling task, in which relevant spans of text are retrieved from sentences, in a similar fashion to how named entity recognition and multiword expressions are handled. In the first half of this dissertation, we propose methods for collecting and annotating datasets of lexical borrowings in context, and apply them to produce COALAS, a novel corpus of Spanish journalistic texts annotated with anglicisms using BIO encoding. We use COALAS to analyze the performance and errors of several supervised sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) and a large language model on a few-shot approach (8B Llama3). Our results show that a BiLSTM-CRF model fed with word and subword embeddings outperforms all other models (including Transformer-based models and the LLM, which performs poorly), and that all models are sensitive in varying degrees to capitalization, sentence position and shape. In the second half of this dissertation, we explore the limitations that standard evaluation based on aggregated metrics produces when evaluating sequence labeling models in general and lexical borrowing identification systems in particular. We propose an evaluation methodology based on formal dimensions to characterize spans in sequence labeling tasks and apply it to build BLAS, a linguistically-motivated benchmark for anglicism identification in Spanish that can predict how a system will generalize to outside data. Finally, we present Observatorio Lázaro, an observatory of anglicism usage in the Spanish press that implements the techniques and resources introduced in the course of this dissertation. The observatory monitors the daily usage of English lexical borrowings in the Spanish press and detects novel anglicisms that appear in Spanish newspapers. As of February 2025, it has registered more than 1,400,000 occurrences of anglicisms since it was launched (with an average of 1,050 anglicisms retrieved daily; 450 of them unique, 45 of those previously unattested), which, to the best of our knowledge, makes Observatorio Lázaro the largest continuously-growing self-populating context-based database of anglicism usage ever compiled. The results of this dissertation show that automatic identification of borrowings can indeed be framed as a sequence labeling task and that this approach is better suited than previous methods for capturing the linguistic nuances of borrowing usage in the wild and can successfully be applied to real-world scenarios.