Semantic role labelling and analysis in economic and cybersecurity contexts using natural language processing classifiers

BackgroundBioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events.ResultsTo evaluate the performance of BIOSMILE, we conducted two experiments to (1) compare the performance of SRL systems trained on newswire and biomedical corpora; and (2) examine the effects of using biomedical-specific features. The experimental results show that using BioProp improves the F-score of the SRL system by 21.45% over an SRL system that uses a newswire corpus. It is noteworthy that adding automatically generated template features improves the overall F-score by a further 0.52%. Specifically, ArgM-LOC, ArgM-MNR, and Arg2 achieve statistically significant performance improvements of 3.33%, 2.27%, and 1.44%, respectively.ConclusionWe demonstrate the necessity of using a biomedical proposition bank for training SRL systems in the biomedical domain. Besides the different characteristics of biomedical and newswire sentences, factors such as cross-domain framesets and verb usage variations also influence the performance of SRL systems. For argument classification, we find that NE (named entity) features indicating if the target node matches with NEs are not effective, since NEs may match with a node of the parsing tree that does not have semantic role labels in the training set. We therefore incorporate templates composed of specific words, NE types, and POS tags into the SRL system. As a result, the classification accuracy for adjunct arguments, which is especially important for biomedical SRL, is improved significantly.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

SRL-ACO: A text augmentation framework based on semantic role labeling and ant colony optimization

Semantic Role Labeling (SRL) is one of the most challenging tasks in Natural Language Processing (NLP). SRL is a task that consists of predicate identification, argument identification and argument classification. In this article we present novel approach for argument classification that is based on deep neural network architecture. Traditional discrete based SRL relies heavily on feature engineering that uses syntactic structures in contrast to deep learning approaches that encode whole sentences without taking into account syntactic features. We present an approach that uses combination of syntactic features and external word representations from FastText. The advantages of using FastText embeddings is the generation of better vector representations for rare words and FastText gives better results for words that are not within the dictionary. These attributes for vector representations give good results for morphologically rich languages. Most of the SRL approaches today are trained on resource rich languages. In this article we present novel neural architecture for SRL that is suitable for resource poor morphology rich languages. Experiments on hr500k corpus shows that our syntax-aware approach shows competitive results for argument classification. We present architecture for argument classification that is based on Bidirectional Long-Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) decoding for finding optimal sequence. Our approach showed results that are very close to benchmark results with F1 score of 72%.

Semantics means the process of extracting precise meaning from text or sentences. Semantic Role Labelling (SRL) plays a crucial role in Natural Language Processing by providing insights into the underlying meaning of sentences. SRL involves assigning generic labels or roles to words within a sentence, indicating their respective semantic roles. It helps in tasks such as information extraction, question answering, educational systems, sentiment analysis, and machine translation by identifying the roles of different words in a sentence and their relationships with each other. This process enables computers to better understand and process human language, leading to more accurate and effective language understanding. SRL also powers AI-driven educational tools like intelligent tutoring systems, personalized learning, and automated assessments, making education more adaptive and effective. In this paper, we are giving a brief review of SRL system developed for different languages. This literature review focuses on key aspects of SRL like techniques used, datasets used, languages for which SRL is developed, and accuracy. Our future work is inclined towards SRL for Hindi, so our review focuses on SRL developed for Hindi, along with other Indian Languages like Urdu, Malayalam and Tamil.

Nominal entity recognition is a fundamental task in natural language processing. Semantic role labeling views a sentence as a predicate-arguments structure, which provides an alternative perspective for the boundary detection and type recognition of nominal entity. In this paper, we propose a nominal entity recognition method with semantic role labeling. First, a maximum entropy (ME) model is trained on unlabeled data to address the data sparse problem in acquiring the preferences for each pair of predicate and argument. Then, use the information of semantic role labeling as features in a high quality nominal entity model. The experiments on ACE 2004 Chinese data show that the proposed method improves the performance of the high quality nominal entity recognizer, and achieves higher accuracy and recall rate.

This chapter presents a brief introduction to entropy guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. The main advantage of ETL is its easy applicability to natural language processing (NLP) tasks. This introductory chapter presents the motivation behind ETL and summarizes our experimental results. In Sect. 1.1, we first briefly detail TBL and explain its bottleneck. Next, we briefly present ETL and list some of its advantages. In Sect. 1.2, we first list some related works on the use of ETL for different NLP tasks. Next, we report a summary of our experimental results on the application of ETL to four language independent NLP tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Finally, in Sect. 1.3, we detail the structure of the book.KeywordsMachine learningEntropy guided transformation learningETL committeeTransformation based learningNatural language processingPart-of-speech taggingPhrase chunkingNamed entity recognitionSemantic role labeling

PNS15 PRAGMATIC ARTIFICIAL INTELLIGENCE-BASED REFERENCE SCREENING IN SYSTEMATIC REVEIWS. ARE TWO ROBOTS BETTER THAN ONE?

In this study, we explore the potential of machine learning (ML) models after different text representation methods on the balanced IMDB dataset, which is widely regarded as a gold standard in sentiment analysis, one of the Natural Language processing (NLP) tasks. On the open source IMDB movie reviews dataset, we first undertake data cleaning and text representation with data preprocessing steps. Then, we apply sentiment classification using different ML models. In order to evaluate the models, we used precision (P), recall (R), F1-score (F1), and area under curve (AUC), as well as receiver operating characteristic (ROC). It is worth noting that text feature extraction with Bidirectional Encoder Representations from Transformers (BERT) provided the highest performance in all models, with the SVM model offering particularly promising results. In this model, we observed the following results: ACC 0.9033, F1 0.9308, R 0.9015, R 0.9015, P 0.9072, AUC 0.9638, and ROC 0.96. These findings suggest that NLP techniques and, in particular, machine learning models that employ BERT may offer high levels of accuracy and reliability in text classification problems. It would be beneficial for future studies to validate these findings using BERT on different NLP tasks. This would help to evaluate the effectiveness and applicability of the models in practice.

Transition-based semantic role labeling with pointer networks

The dominant paradigm in Natural Language Processing (NLP) has traditionally relied on sequential modelling, often overlooking the rich, non-linear structural dependencies inherent in linguistic data. Graph Neural Networks (GNNs) offer a powerful alternative by modelling text as structured graphs, yet a systematic evaluation of their comparative efficacy across tasks of varying structural complexity remains under-explored. This paper presents a comprehensive comparative study of three prominent GNN architectures- Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph SAGE- applied to three distinct NLP tasks: Text Classification, Semantic Role Labelling (SRL), and Abstract Meaning Representation (AMR) Parsing. We construct task-specific graph topologies, utilizing heterogeneous corpus graphs for document classification and syntactic dependency trees for SRL and AMR. Our empirical results, derived from benchmarks on the 20 Newsgroups, CoNLL-2009, and AMR 2.0 datasets, reveal a clear correlation between architectural properties and task complexity. While GCNs provide a robust and efficient baseline for global text classification (Accuracy: 96.6%), they struggle with the fine-grained structural modelling required for semantic tasks. Conversely, GATs demonstrate superior performance on syntax-heavy tasks, achieving a Labelled F1 score of 85.8% on SRL and a Smatch score of 74.5 on AMR parsing, significantly outperforming the GCN baseline (82.5% and 71.2%, respectively). These findings suggest that the anisotropic aggregation capability of attention mechanisms is critical for capturing the nuanced, long-range dependencies in natural language, establishing GAT as the preferred architecture for structure-dependent NLP applications.

Semantic role labeling (SRL) is a well known task in Natural Language Processing, consisting of identifying and labeling verbal arguments. It has been widely studied in English, but scarcely explored in other languages. In this paper, we employ a two-step convolutional neural architecture to label semantic arguments in Brazilian Portuguese texts, and avoid the use of external NLP tools. We achieve an F1 score of 62.2, which, although considerably lower than the state-of-the-art for English, seems promising considering the available resources. Also, dividing the process into two easier subtasks makes it more feasible to further improve performance through semi-supervised learning. Our system is available online and ready to be used out of the box to label new texts.

2043 Background: Electronic health records (EHR) are used for retrospective cancer outcomes analysis. Sites and timing of recurrence are not captured in structured EHR data. Novel computerized methods are necessary to use unstructured longitudinal EHR data for large scale studies. Methods: We previously developed a neural network-based NLP algorithm to identify no recurrence vs. metastatic recurrence cases by analyzing physician notes, pathology and radiology reports in Stanford’s breast cancer database, Oncoshare (Cohort A). To validate this algorithm for local vs. distant recurrence, we identified a distinct Oncoshare cohort (Cohort B). Cases were manually curated for longitudinal development of local or distant recurrence and metastatic sites. A two-sided t-test was used to compare mean probabilities between local and distant recurrence cases. Next, we combined cases in Cohorts A and B to train and validate a novel NLP classifier that identifies metastatic site. The combined cohort was randomly divided into training and validation sets. Sensitivity and specificity were calculated for the NLP algorithm’s ability to detect metastatic sites compared to manual curation. Results: In Cohort B: 350 metastatic cases were identified. Mean probability for local and distant recurrence was 0.43 and 0.79, respectively and differed significantly for patients with local vs. distant recurrence (p<0.01). In Cohorts A and B: 632 metastatic cases were used for determination of sites. Sensitivity and specificity were highest for detection of peritoneal metastasis followed by liver, lung, skin, bone and central nervous system (table). Conclusions: This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site. This method may facilitate analysis of large datasets and correlation of outcomes with metastatic site. [Table: see text]

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable.

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of shortest-path inference, on the assumption that the SRL model is restricted to arc-factored features of the syntactic paths behind semantic roles. Overall, our method for SRL is a novel way to exploit larger variability in the syntactic realizations of predicate-argument relations, moving away from pipeline architectures. Experiments show that our approach improves the robustness of the predictions, producing arc-factored models that perform closely to methods using unrestricted features from the syntax.