Semantic Similarity Research Articles

Text Summarization Based on Semantic Similarity

In the contemporary information age, the sheer volume of textual data poses a significant challenge for efficient comprehension and utilization. This project endeavors to address this challenge by developing a Text Summarization System grounded in semantic similarities. The primary goal is to create a robust and intuitive tool that extracts key information from large textual datasets, offering users a concise and meaningful summary. The proposed system employs advanced Natural Language Processing (NLP) techniques to analyze the semantic relationships within the text. Rather than relying solely on syntactic structures, the model identifies and leverages semantic similarities, such as shared concepts, themes, and contextual relationships, to distill the essential content. This approach enhances the summarization process by ensuring that the generated summaries reflect a deeper understanding of the underlying semantics, thereby capturing the core meaning of the text. Throughout the development of this project, the B.Tech student will delve into the intricacies of semantic analysis, exploring techniques to recognize and prioritize key concepts. The system's effectiveness will be evaluated through rigorous testing on diverse textual datasets, assessing its ability to generate coherent and relevant summaries across various domains. This project not only contributes to the field of NLP but also has practical applications in information retrieval, document summarization, and content curation. By providing an innovative solution to the challenges of information overload, the Text Summarization System based on semantic similarities offers a valuable tool for enhancing efficiency in information processing and decision-making. Index terms Text Summarization, Semantic Similarities, Natural Language Processing (NLP), Semantic Analysis, Information Retrieval, Document Summarization, Content Curation, Information Overload, Decision Making, Textual Data Analysis, Key Concept Recognition, Conceptual Relationships, Syntactic Structures, Semantic Understanding, Textual Datasets Evalution.

Supervised Contrastive Discrete Hashing for cross-modal retrieval

Cross-modal hashing aims at mapping data points from varied modalities to a shared Hamming space for providing swift and adaptive search services. The recent progress in contrastive learning has significantly enhanced the retrieval performance of cross-modal hashing, owing to its strong ability in powerful representations learning. However, most of them face two primary challenges: (1) they select negative samples for anchors randomly in each mini-batch, which may bring false negative samples that destroy the intrinsic semantic similarity in the learned hash codes; (2) the continuous relaxation strategy is commonly used to generate discrete hash codes, which results in quantization errors and consequently impairing the quality of hash codes. To alleviate these issues, we propose a deep supervised cross-modal hashing scheme, termed Supervised Contrastive Discrete Hashing (SCDH). Specifically, we extend the unsupervised contrastive method to the supervised setting to construct the positive and negative data points with class labels, avoiding the performance decreasing caused by false negative data. Besides, multiple positive and negative samples for each anchor can be chosen from the generated global dictionary bank by class labels, which can better capture the global data structure. Moreover, discrete hash codes can be generated directly, enhancing the feature representational capacity of hash codes. Comprehensive experiments conducted on two public datasets affirm the superior performance of the proposed SCDH over several other cutting-edge methods.

GEHGAN[formula omitted] CircRNA–disease association prediction via graph embedding and heterogeneous graph attention network

There is growing proof suggested that circRNAs play a crucial function in diverse important biological reactions related to human diseases. Within the area of biochemistry, a massive range of wet experiments have been carried out to find out the connections of circRNA–disease in recent years. Since wet experiments are expensive and laborious, nowadays, calculation-based solutions have increasingly attracted the attention of researchers. However, the performance of these methods is restricted due to the inability to balance the distribution among various types of nodes. To remedy the problem, we present a novel computational method called GEHGAN to forecast the new relationships in this research, leveraging graph embedding and heterogeneous graph attention networks. Firstly, we calculate circRNA sequences similarity, circRNA RBP similarity, disease semantic similarity and corresponding GIP kernel similarity to construct heterogeneous graph. Secondly, a graph embedding method using random walks with jump and stay strategies is applied to obtain the preliminary embeddings of circRNAs and diseases, greatly improving the performance of the model. Thirdly, a multi-head graph attention network is employed to further update the embeddings, followed by the employment of the MLP as a predictor. As a result, the five-fold cross-validation indicates that GEHGAN achieves an outstanding AUC score of 0.9829 and an AUPR value of 0.9815 on the CircR2Diseasev2.0 database, and case studies on osteosarcoma, gastric and colorectal neoplasms further confirm the model’s efficacy at identifying circRNA–disease correlations.

Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study.

Healthcare providers currently calculate risk of the composite outcome of morbidity or mortality associated with a coronary artery bypass grafting (CABG) surgery through manual input of variables into a logistic regression-based risk calculator. This study indicates that automated artificial intelligence (AI)-based techniques can instead calculate risk. Specifically, we present novel numerical embedding techniques that enable NLP (natural language processing) models to achieve higher performance than the risk calculator using a single preoperative surgical note. The most recent preoperative surgical consult notes of 1,738 patients who received an isolated CABG from July 1, 2014 to November 1, 2022 at a single institution were analyzed. The primary outcome was the Society of Thoracic Surgeons defined composite outcome of morbidity or mortality (MM). We tested three numerical-embedding techniques on the widely used TextCNN classification model: 1a) Basic embedding, treat numbers as word tokens; 1b) Basic embedding with a dataloader that Replaces out-of-context (ROOC) numbers with a tag, where context is defined as within a number of tokens of specified keywords; 2) ScaleNum, an embedding technique that scales in-context numbers via a learned sigmoid-linear-log function; and 3) AttnToNum, a ScaleNum-derivative that updates the ScaleNum embeddings via multi-headed attention applied to local context. Predictive performance was measured via area under the receiver operating characteristic curve (AUC) on holdout sets from 10 random-split experiments. For eXplainable-AI (X-AI), we calculate SHapley Additive exPlanation (SHAP) values at an ngram resolution (SHAP-N). While the analyses focus on TextCNN, we execute an analogous performance pipeline with a long short-term memory (LSTM) model to test if the numerical embedding advantage is robust to model architecture. A total of 567 (32.6%) patients had MM following CABG. The embedding performances are as follows with the TextCNN architecture: 1a) Basic, mean AUC 0.788 [95% CI (confidence interval): 0.768-0.809]; 1b) ROOC, 0.801 [CI: 0.788-0.815]; 2) ScaleNum, 0.808 [CI: 0.785-0.821]; and 3) AttnToNum, 0.821 [CI: 0.806-0.834]. The LSTM architecture produced a similar trend. Permutation tests indicate that AttnToNum outperforms the other embedding techniques, though not statistically significant verse ScaleNum (p-value of .07). SHAP-N analyses indicate that the model learns to associate low blood urine nitrate (BUN) and creatinine values with survival. A correlation analysis of the attention-updated numerical embeddings indicates that AttnToNum learns to incorporate both number magnitude and local context to derive semantic similarities. This research presents both quantitative and clinical novel contributions. Quantitatively, we contribute two new embedding techniques: AttnToNum and ScaleNum. Both can embed strictly positive and bounded numerical values, and both surpass basic embeddings in predictive performance. The results suggest AttnToNum outperforms ScaleNum. With regards to clinical research, we show that AI methods can predict outcomes after CABG using a single preoperative note at a performance that matches or surpasses the current risk calculator. These findings reveal the potential role of NLP in automated registry reporting and quality improvement.

How the Listener’s Attention Dynamically Switches Between Different Speakers During a Natural Conversation

The neural mechanisms underpinning the dynamic switching of a listener’s attention between speakers are not well understood. Here we addressed this issue in a natural conversation involving 21 triadic adult groups. Results showed that when the listener’s attention dynamically switched between speakers, neural synchronization with the to-be-attended speaker was significantly enhanced, whereas that with the to-be-ignored speaker was significantly suppressed. Along with attention switching, semantic distances between sentences significantly increased in the to-be-ignored speech. Moreover, neural synchronization negatively correlated with the increase in semantic distance but not with acoustic change of the to-be-ignored speech. However, no difference in neural synchronization was found between the listener and the two speakers during the phase of sustained attention. These findings support the attenuation model of attention, indicating that both speech signals are processed beyond the basic physical level. Additionally, shifting attention imposes a cognitive burden, as demonstrated by the opposite fluctuations of interpersonal neural synchronization.

Evaluating Sentence‐BERT‐powered learning analytics for automated assessment of students' causal diagrams

AbstractBackgroundWhen learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real‐time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the responses and their position in the causal chain. However, the responsible adoption and effectiveness of automated diagram assessment depend on its reliability.ObjectivesIn this study, we compare two Dutch pre‐trained models (i.e., based on RobBERT and BERTje) in combination with two machine‐learning classifiers—Support Vector Machine (SVM) and Neural Networks (NN), in terms of different indicators of automated diagram assessment reliability. We also contrast two techniques (i.e., semantic similarity and machine learning) for estimating the correct position of a student diagram response in the causal chain.MethodsFor training and evaluation of the models, we capitalize on a human‐labelled dataset containing 2900+ causal diagrams completed by 700+ secondary school students, accumulated from previous diagramming experiments.Results and ConclusionsIn predicting correct responses, 86% accuracy and Cohen's κ of 0.69 were reached, with combinations using SVM being roughly three‐times faster (important for real‐time applications) than their NN counterparts. In terms of predicting the response position in the causal diagrams, 92% accuracy and 0.89 Cohen's κ were reached.ImplicationsTaken together, these evaluation figures equip educational designers for decision‐making on when these NLP‐powered learning analytics are warranted for automated formative feedback in causal relation learning; thereby potentially enabling real‐time feedback for learners and reducing teachers' workload.

Mitigating biases in long-tailed recognition via semantic-guided feature transfer

Dealing with significant class imbalance poses a significant challenge in various real-world applications, particularly when the accurate classification and generalization of minority classes are of crucial. One key factor that results in model biases is the inadequate representation of intra-class diversity in samples from tail classes. To tackle this challenge, transfer learning strategies have emerged as a viable approach to promote the data distribution of tail classes by transferring feature learned from head classes. However, existing methods often assume that transferability exists between any head class and tail class, and randomly transfer information during training, which can be problematic when the head and tail classes have uncorrelated semantics. To tackle this problem, we propose a novel feature transfer strategy to promote the learning of discriminative representations for tail classes. Firstly, we compute the semantic similarity in a hybrid manner by combining local visual features and global semantic features. Secondly, we utilize the Gaussian mixture model to represent each class with multiple and anisotropic prototypes, which helps us fit the actual distribution accurately. We then carry out distribution-aware head-to-tail feature transfer, leveraging an optimization objective that can derive a computationally efficient closed-form upper bound. This procedure mitigates representation biases in the feature space and achieves state-of-the-art results on CIFAR100-LT, ImageNet-LT, and iNaturalist long-tailed benchmarks.

Multi-grained Representation Aggregating Transformer with Gating Cycle for Change Captioning

Change captioning aims to describe the difference within an image pair in natural language, which combines visual comprehension and language generation. Although significant progress has been achieved, it remains a key challenge of perceiving the object change from different perspectives, especially the severe situation with drastic viewpoint change. In this paper, we propose a novel full-attentive network, namely Multi-grained Representation Aggregating Transformer (MURAT), to distinguish the actual change from viewpoint change. Specifically, the Pair Encoder first captures similar semantics between pairwise objects in a multi-level manner, which are regarded as the semantic cues of distinguishing the irrelevant change. Next, a novel Multi-grained Representation Aggregator (MRA) is designed to construct the reliable difference representation by employing both coarse- and fine-grained semantic cues. Finally, the language decoder generates a description of the change based on the output of MRA. Besides, the Gating Cycle Mechanism is introduced to facilitate the semantic consistency between difference representation learning and language generation with a reverse manipulation, so as to bridge the semantic gap between change features and text features. Extensive experiments demonstrate that the proposed MURAT can greatly improve the ability to describe the actual change in the distraction of irrelevant change and achieves state-of-the-art performance on three benchmarks, CLEVR-Change, CLEVR-DC and Spot-the-Diff.

Semantic Similarity Caculating based on BERT

The exploration of semantic similarity is a fundamental aspect of natural language processing, as it aids in comprehending the significance and usage of vocabulary present in a language. The advent of pre-training language models has significantly simplified the process of research in this field. This article delves into the methodology of utilizing the pre-trained language model, BERT, to calculate the semantic similarity among Chinese words. In order to conduct this study, we first trained our own model using the bert-base-chinese pre-trained model. This allowed us to acquire the word embeddings for every single word, which served as the basis for calculating semantic similarity. Essentially, word embeddings are vector-based depictions of words that encapsulate word’s significance and surroundings, allowing for the measurement of the semantic similarity between words. Next, we executed a sequence of experiments to assess the efficiency of the BERT model in managing semantic similarity tasks within the Chinese language. The results were encouraging, as the BERT model demonstrated remarkable performance in these tasks. Furthermore, it was observed that the BERT model outperformed traditional methods in terms of performance and generalization capabilities. This study, therefore, underscores the potential of the BERT model in natural language processing, particularly in the Chinese language. This emphasizes the model’s capacity to accurately calculate semantic similarity, paving the way for its widespread adoption in related fields.

Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods

Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.

An improved deep hashing model for image retrieval with binary code similarities

The exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms.

A Span-based Multivariate Information-aware Embedding Network for joint relational triplet extraction of threat intelligence

The extraction of relational triplets in threat intelligence is a critical aspect of constructing a knowledge graph. However, the field encounters challenges like high semantic similarity among entities, limited relevance of entities, and a heavy reliance on experts, leading to low extraction efficiency. Currently, there is a lack of research on extracting threat intelligence relational triplets, which necessitates the development of an efficient extraction model. This study proposes a Span-based Multivariate Information-aware Embedding Network (SMIEN) for the joint extraction of threat intelligence relation triplets. SMIEN introduces aware embedding modules to capture fine-grained features of multivariate information, including semantic, temporal order, dependency, spans, span pairs, entity labels, and relational labels, and to enhance their interaction. Designed a Type-Aware Graph Convolutional Network (TA-GCN) to enhance the representation of key information between less relevant tokens in threat intelligence text sentences within the Dependency-Aware Embedding layer. The Entity-Aware Embedding module is designed to enhance fine-grained interaction between span information and other multivariate information, while the Relationship-Aware Embedding module is designed to determine correlations between span pairs and enhance interaction with relationship labels. Experimental results on the HACKER, RE-DNRTI, and RE-IVTI datasets demonstrate micro F1 scores of 44.73%, 81.74%, and 44.15%, respectively, highlighting the method’s effectiveness in extracting cyber threat intelligence and Internet of Vehicle (IoV) threat intelligence information. In addition, we have constructed a threat intelligence ontology (CVTIO) on which we have built a preliminary threat intelligence knowledge graph (CVTIKG) with 23,636 relational triplets. The CVTIKG can be found at https://github.com/wangxtz/CVTIKG.

Media Buzzword Analysis Integrated with Phrase Vectors and Topic Model

Buzzword analysis is one of the important research contents of natural language processing, and the research results can provide technical support for public opinion analysis. The purpose of extracting media buzzwords is to analyze the rules and changes of language change within a range. The traditional word feature-based buzzword extraction had some problems, such as low accuracy and low coverage, and this paper proposes a media buzzword analysis based on the combination of phrase vector and topic model, the core idea is to integrate the semantic similarity features, and use visualization technology to more intuitively show the overall language change rules. Visualization analyses uses a large number of corpus statistics, calculate the distance between words, and then convert into similarity, through word similarity calculation to show the distribution relationship between different words, and finally quantitative perspective to analyze. Our model is better than the traditional system, and the research results can provide corpus and model support for subsequent research directions.

The organization of semantic associations between senses in language

Abstract Distributional semantic representations were used to investigate crossmodal correspondences within language, offering a comprehensive analysis of how sensory experiences interconnect in linguistic constructs. By computing semantic proximity between words from different sensory modalities, a crossmodal semantic network was constructed, providing a general view of crossmodal correspondences in the English language. Community detection techniques were applied to unveil domains of experience where crossmodal correspondences were likely to manifest, while also considering the role of affective dimensions in shaping these domains. The study revealed the existence of an architecture of structured domains of experience in language, whereby crossmodal correspondences are deeply embedded. The present research highlights the roles of emotion and statistical associations in the organization of sensory concepts across modalities in language. The domains identified, including food, the body, the physical world and emotions/values, underscored the intricate interplay between the senses, emotion and semantic patterns. These findings align with the embodied lexicon hypothesis and the semantic coding hypothesis, emphasizing the capacity of language to capture and reflect crossmodal correspondences’ emotional and perceptual subtleties in the form of networks, while also revealing opportunities for further perceptual research on crossmodal correspondences and multisensory integration.

What Do Self-Supervised Speech Models Know About Words?

Abstract Many self-supervised speech models (S3Ms) have been introduced over the last few years, improving performance and data efficiency on various speech tasks. However, these empirical successes alone do not give a complete picture of what is learned during pre-training. Recent work has begun analyzing how S3Ms encode certain properties, such as phonetic and speaker information, but we still lack a proper understanding of knowledge encoded at the word level and beyond. In this work, we use lightweight analysis methods to study segment-level linguistic properties—word identity, boundaries, pronunciation, syntactic features, and semantic features—encoded in S3Ms. We present a comparative study of layer-wise representations from ten S3Ms and find that (i) the frame-level representations within each word segment are not all equally informative, and (ii) the pre-training objective and model size heavily influence the accessibility and distribution of linguistic information across layers. We also find that on several tasks—word discrimination, word segmentation, and semantic sentence similarity—S3Ms trained with visual grounding outperform their speech-only counterparts. Finally, our task-based analyses demonstrate improved performance on word segmentation and acoustic word discrimination while using simpler methods than prior work.1

Text-image semantic relevance identification for aspect-based multimodal sentiment analysis

Aspect-based multimodal sentiment analysis (ABMSA) is an emerging task in the research of multimodal sentiment analysis, which aims to identify the sentiment of each aspect mentioned in multimodal sample. Although recent research on ABMSA has achieved some success, most existing models only adopt attention mechanism to interact aspect with text and image respectively and obtain sentiment output through multimodal concatenation, they often neglect to consider that some samples may not have semantic relevance between text and image. In this article, we propose a Text-Image Semantic Relevance Identification (TISRI) model for ABMSA to address the problem. Specifically, we introduce a multimodal feature relevance identification module to calculate the semantic similarity between text and image, and then construct an image gate to dynamically control the input image information. On this basis, an image auxiliary information is provided to enhance the semantic expression ability of visual feature representation to generate more intuitive image representation. Furthermore, we employ attention mechanism during multimodal feature fusion to obtain the text-aware image representation through text-image interaction to prevent irrelevant image information interfering our model. Experiments demonstrate that TISRI achieves competitive results on two ABMSA Twitter datasets, and then validate the effectiveness of our methods.

Flexible and fine-grained simulation of speed in language processing.

According to the embodied cognition theory, language comprehension is achieved through mental simulation. This account is supported by a number of studies reporting action simulations during language comprehension. However, which details of sensory-motor experience are included in these simulations is still controversial. Here, three experiments were carried out to examine the simulation of speed in action language comprehension. Experiment 1 adopted a lexical decision task and a semantic similarity judgment task on isolated fast and slow action verbs. It has been shown that fast action verbs were processed significantly faster than slow action verbs when deep semantic processing is required. Experiment 2 and Experiment 3 investigated the contextual influence on the simulation of speed, showing that the processing of verbs, either depicting fast actions or neutral actions, would be slowed down when embedded in the slow action sentences. These experiments together demonstrate that the fine-gained information, speed, is an important part of action representation and can be simulated but may not in an automatic way. Moreover, the speed simulation is flexible and can be modulated by the context.

Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis.

Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. "Oddities" in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis. Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups. We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a 'typical linguistic profile (TLP)'. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP - early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP - stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness). The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of 'formal thought disorder'.

Quantifying task-related gaze.

Competing theories attempt to explain what guides eye movements when exploring natural scenes: bottom-up image salience and top-down semantic salience. In one study, we apply language-based analyses to quantify the well-known observation that task influences gaze in natural scenes. Subjects viewed ten scenes as if they were performing one of two tasks. We found that the semantic similarity between the task and the labels of objects in the scenes captured the task-dependence of gaze (t(39) = 13.083; p < 0.001). In another study, we examined whether image salience or semantic salience better predicts gaze during a search task, and if viewing strategies are affected by searching for targets of high or low semantic relevance to the scene. Subjects searched 100 scenes for a high- or low-relevance object. We found that image salience becomes a worse predictor of gaze across successive fixations, while semantic salience remains a consistent predictor (X2(1, N=40) = 75.148, p < .001). Furthermore, we found that semantic salience decreased as object relevance decreased (t(39) = 2.304; p = .027). These results suggest that semantic salience is a useful predictor of gaze during task-related scene viewing, and that even in target-absent trials, gaze is modulated by the relevance of a search target to the scene in which it might be located.

Automated Marking System for Essay Questions

The stress of marking assessment scripts of many candidates often results in fatigue that could lead to low productivity and reduced consistency. In most cases, candidates use words, phrases and sentences that are synonyms or related in meaning to those stated in the marking scheme, however, examiners rely solely on the exact words specified in the marking scheme. This often leads to inconsistent grading and in most cases, candidates are disadvantaged. This study seeks to address these inconsistencies during assessment by evaluating the marked answer scripts and the marking scheme of Introduction to File Processing (CSC 221) from the Department of Computer Science, University of Uyo, Nigeria. These were collected and used with the Microsoft Research Paraphrase (MSRP) corpus. After preprocessing the datasets, they were subjected to Logistic Regression (LR), a machine learning technique where the semantic similarity of the answers of the candidates was measured in relation to the marking scheme of the examiner using the MSRP corpus model earlier trained on the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Results of the experiment show a strong correlation coefficient of 0.89 and a Mean Relative Error (MRE) of 0.59 compared with the scores awarded by the human marker (examiner). Analysis of the error indicates that block marks were assigned to answers in the marking scheme while the automated marking system breaks the block marks into chunks based on phrases both in the marking scheme and the candidates’ answers. It also shows that some semantically related words were ignored by the examiner.