Sometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the ...Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part‐of‐speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.
•A novel guiding mechanism based on Part of Speech (PoS) tags is proposed for guiding image caption generation process.•The framework achieves competitive performance over state-of-the-art models on ...Flickr30K and MS COCO datasets.•Results including some demonstrative examples are provided to show the effectiveness of the novel guiding mechanism.
As a fundamental problem in image understanding, image caption generation has attracted much attention from both computer vision and natural language processing communities. In this paper, we focus on how to exploit the structure information of a natural sentence, which is used to describe the content of an image. We discover that the Part of Speech (PoS) tags of a sentence, are very effective cues for guiding the Long Short-Term Memory (LSTM) based word generator. More specifically, given a sentence, the PoS tag of each word is utilized to determine whether it is essential to input image representation into the word generator. Benefiting from such a strategy, our model can closely connect the visual attributes of an image to the word concepts in the natural language space. Experimental results on the most popular benchmark datasets, e.g., Flickr30k and MS COCO, consistently demonstrate that our method can significantly enhance the performance of a standard image caption generation model, and achieve the conpetitive results.
Relation classification plays an important role in the field of natural language processing (NLP). Previous research on relation classification has verified the effectiveness of using convolutional ...neural network (CNN) and recurrent neural network (RNN). In this paper, we proposed a model that combine the RNN and CNN (RCNN), which will Give full play to their respective advantages: RNN can learn temporal and context features, especially long-term dependency between two entities, while CNN is capable of catching more potential features. We experiment our model on the SemEval-2010 Task 8 dataset1, and the result shows that our method is superior to most of the existing methods.
Change captioning aims to describe the semantic change between two similar images. In this process, as the most typical distractor, viewpoint change leads to the pseudo changes about appearance and ...position of objects, thereby overwhelming the real change. Besides, since the visual signal of change appears in a local region with weak feature, it is difficult for the model to directly translate the learned change features into the sentence. In this paper, we propose a syntax-calibrated multi-aspect relation transformer to learn effective change features under different scenes, and build reliable cross-modal alignment between the change features and linguistic words during caption generation. Specifically, a multi-aspect relation learning network is designed to 1) explore the fine-grained changes under irrelevant distractors (e.g., viewpoint change) by embedding the relations of semantics and relative position into the features of each image; 2) learn two view-invariant image representations by strengthening their global contrastive alignment relation, so as to help capture a stable difference representation; 3) provide the model with the prior knowledge about whether and where the semantic change happened by measuring the relation between the representations of captured difference and the image pair. Through the above manner, the model can learn effective change features for caption generation. Further, we introduce the syntax knowledge of Part-of-Speech (POS) and devise a POS-based visual switch to calibrate the transformer decoder. The POS-based visual switch dynamically utilizes visual information during different word generation based on the POS of words. This enables the decoder to build reliable cross-modal alignment, so as to generate a high-level linguistic sentence about change. Extensive experiments show that the proposed method achieves the state-of-the-art performance on the three public datasets.
Dual Adversarial Transfer for Sequence Labeling Zhou, Joey Tianyi; Zhang, Hao; Jin, Di ...
IEEE transactions on pattern analysis and machine intelligence,
2021-Feb.-1, 2021-Feb, 2021-2-1, 20210201, Letnik:
43, Številka:
2
Journal Article
Recenzirano
We propose a new architecture for addressing sequence labeling, termed Dual Adversarial Transfer Network (DATNet). Specifically, the proposed DATNet includes two variants, i.e., DATNet-F and ...DATNet-P, which are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD) and adopt adversarial training to boost model generalization. We investigate the effects of different components of DATNet across different domains and languages, and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve state-of-the-art performances on CoNLL, Twitter, PTB-WSJ, OntoNotes and Universal Dependencies with three popular sequence labeling tasks, i.e., Named entity recognition (NER), Part-of-Speech (POS) Tagging and Chunking.
Aspect sentiment triplet extraction is an extremely daunting task designed to identify the triplets from comments, where each triplet is composed of an aspect term, the related opinion term, and the ...sentiment between them. Existing research efforts majorly construct a novel tagging scheme to avoid the disadvantages of pipeline methods. However, the improvement is limited due to neglecting the implicit grammatical relationships among the three elements in a triplet. To cope with this limitation, we put forward an innovative Dependency Graph Enhanced Interactive Attention Network, which explicitly introduces the syntactic and semantic relationships between words. Specifically, an interactive attention mechanism is conceived to jointly consider both the contextual features learned from Bi-directional Long Short-Term Memory and the syntactic dependencies learned from the correspondent dependency graph in an iterative interaction manner. In addition, we notice that words with different Part-of-Speech categories have different contributions to the semantic expression of sentences. Accordingly, the information of different Part-of-Speech categories is recognized during the modeling process to properly capture the semantic relationships. Experiments on the benchmark datasets originally derived from SemEval Challenges illustrate that our presented approach has superiority over strong baselines.
Chinese part‐of‐speech (POS) tagging is an essential task for Chinese downstream natural language processing tasks. The accuracy of the Chinese POS task will drop dramatically by word‐based methods ...because of the segmentation errors and the word sparsity. Also, there are several Chinese POS tagging sets with different criteria. Some of them only have a small‐scale annotated corpus and are hard to train. To this end, we propose a modified word‐based transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word‐based model, instead of adopting a joint character‐based method. Extensive experiments show that our method achieves state‐of‐the‐art performance on all datasets, and more importantly, our method improves performance effectively for the word‐based Chinese sequence labeling task.
With the development of the Internet, the issue of cyberbullying on social media has gained significant attention. Cyberbullying is often expressed in text. Methods of identifying such text via ...machine learning have been growing, most of which rely on the extraction of part-of-speech (POS) tags to improve their performance. However, the current study only arbitrarily used part-of-speech labels that it considered reasonable, without investigating whether the chosen part-of-speech labels can better enhance the effectiveness of the cyberbullying detection task. In other words, the effectiveness of different part-of-speech labels in the automatic cyberbullying detection task was not proven. This study aimed to investigate the part of speech in statements related to cyberbullying and explore how three classification models (random forest, naïve Bayes, and support vector machine) are sensitive to parts of speech in detecting cyberbullying. We also examined which part-of-speech combinations are most appropriate for the models mentioned above. The results of our experiments showed that the predictive performance of different models differs when using different part-of-speech tags as inputs. Random forest showed the best predictive performance, and naive Bayes and support vector machine followed, respectively. Meanwhile, across the different models, the sensitivity to different part-of-speech tags was consistent, with greater sensitivity shown towards nouns, verbs, and measure words, and lower sensitivity shown towards adjectives and pronouns. We also found that the combination of different parts of speech as inputs had an influence on the predictive performance of the models. This study will help researchers to determine which combination of part-of-speech categories is appropriate to improve the accuracy of cyberbullying detection.
To generate an image caption, firstly, the content of the image should be fully understood; and then the semantic information contained in the image should be described using a phrase or statement ...that conforms to certain grammatical rules. Thus, it requires techniques from both computer vision and natural language processing to connect the two different media forms together, which is highly challenging. To adaptively adjust the effect of visual information and language information on the captioning process, in this paper, the part of speech information is proposed to novelly integrate with image captioning models based on the encoder-decoder framework. First, a part of speech prediction network is proposed to analyze and model the part of speech sequences for the words in natural language sentences; then, different mechanisms are proposed to integrate the part of speech guidance information with merge-based and inject-based image captioning models, respectively; finally, according to the integrated frameworks, a multi-task learning paradigm is proposed to facilitate model training. Experiments are conducted on two widely used image captioning datasets, Flickr30 k and COCO, and the results have validated that the image captions generated by the proposed method contain more accurate visual information and comply with language habits and grammar rules better.
Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text ...analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully.
PDF
