Reliable and Interpretable Drift Detection in Streams of Short Texts

Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.Comment: ACL2023 industry track (9 pages

Distance,Time and Terms in First Story Detection

First Story Detection (FSD) is an important application of online novelty detection within Natural Language Processing (NLP). Given a stream of documents, or stories, about news events in a chronological order, the goal of FSD is to identify the very first story for each event. While a variety of NLP techniques have been applied to the task, FSD remains challenging because it is still not clear what is the most crucial factor in defining the “story novelty”. Giventhesechallenges,thethesisaddressedinthisdissertationisthat the notion of novelty in FSD is multi-dimensional. To address this, the work presented has adopted a three dimensional analysis of the relative qualities of FSD systems and gone on to propose a specific method that wearguesignificantlyimprovesunderstandingandperformanceofFSD. FSD is of course not a new problem type; therefore, our first dimen sion of analysis consists of a systematic study of detection models for firststorydetectionandthedistancesthatareusedinthedetectionmod els for defining novelty. This analysis presents a tripartite categorisa tion of the detection models based on the end points of the distance calculation. The study also considers issues of document representation explicitly, and shows that even in a world driven by distributed repres iv entations,thenearestneighbourdetectionmodelwithTF-IDFdocument representations still achieves the state-of-the-art performance for FSD. Weprovideanalysisofthisimportantresultandsuggestpotentialcauses and consequences. Events are introduced and change at a relatively slow rate relative to the frequency at which words come in and out of usage on a docu ment by document basis. Therefore we argue that the second dimen sion of analysis should focus on the temporal aspects of FSD. Here we are concerned with not only the temporal nature of the detection pro cess, e.g., the time/history window over the stories in the data stream, but also the processes that underpin the representational updates that underpin FSD. Through a systematic investigation of static representa tions, and also dynamic representations with both low and high update frequencies, we show that while a dynamic model unsurprisingly out performs static models, the dynamic model in fact stops improving but stays steady when the update frequency gets higher than a threshold. Our third dimension of analysis moves across to the particulars of lexicalcontent,andcriticallytheaffectoftermsinthedefinitionofstory novelty. Weprovideaspecificanalysisofhowtermsarerepresentedfor FSD, including the distinction between static and dynamic document representations, and the affect of out-of-vocabulary terms and the spe cificity of a word in the calculation of the distance. Our investigation showed that term distributional similarity rather than scale of common v terms across the background and target corpora is the most important factor in selecting background corpora for document representations in FSD. More crucially, in this work the simple idea of the new terms emerged as a vital factor in defining novelty for the first story

A Deep Learning Anomaly Detection Method in Textual Data

In this article, we propose using deep learning and transformer architectures combined with classical machine learning algorithms to detect and identify text anomalies in texts. Deep learning model provides a very crucial context information about the textual data which all textual context are converted to a numerical representation. We used multiple machine learning methods such as Sentence Transformers, Auto Encoders, Logistic Regression and Distance calculation methods to predict anomalies. The method are tested on the texts data and we used syntactic data from different source injected into the original text as anomalies or use them as target. Different methods and algorithm are explained in the field of outlier detection and the results of the best technique is presented. These results suggest that our algorithm could potentially reduce false positive rates compared with other anomaly detection methods that we are testing.Comment: 8 Pages, 4 Figure

Innovation Novelty and Firm Value: Deep Learning based Text Understanding

Innovation is widely acknowledged as a key driver of firm performance, with patents serving as unique indicators of a company’s technological advancements. This study aims to investigate the impact of textual novelty within patents on firm performance, focusing specifically on biotechnology startups listed on the Nasdaq. Utilizing deep learning-based approaches, we construct measures for semantic originality in patent texts. Through panel vector autoregressive (VAR) analysis, our empirical findings demonstrate a positive correlation between textual novelty and abnormal stock returns. Further, impulse response function analysis indicates that the impact of textual novelty peaks approximately one week after patent issuance and gradually diminishes within a month. These insights offer valuable contributions to both the theoretical understanding and practical application of innovation management and strategic planning

Computational explorations of semantic cognition

Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the models’ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from “healthy” models, and generate “lesioned” models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words