Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

Word sense disambiguation using semantic relatedness measurement

All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to WordNet’s synsets cooperating with WordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in WordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed con- cepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.

Chinese multi-document personal name disambiguation

This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed： the personal name Boolean vectors denoting whether a personal name occurs the text the biographical word Boolean vector representing title, occupation and so forth, and the feature vector with real values. Then, by combining a heuristic strategy based on Boolean vectors with an agglomeratie clustering algorithm based on feature vectors, it seeks to resolve multi-document personal name coreference. Experimental results show that this approach achieves a good performance by testing on ＂Wang Gang＂ corpus.

Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in Chinese.To solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge integration.Particularly,a graph model combining various Chinese and English knowledge resources by word sense mapping is designed.Firstly,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet.Then,English word similarity is computed based on English word embeddings and knowledge base.Chinese word similarity is evaluated with Chinese word embedding and HowNet,respectively.The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities,which are utilized to construct a disambiguation graph.The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words.Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.

WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS

Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.

Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation

It’s common that different individuals share the same name, which makes it time-consuming to search information of a particular individual on the web. Name disambiguation study is necessary to help users find the person of interest more readily. In this paper, we propose an Adaptive Resonance Theory (ART) based two-stage strategy for this problem. We get a first-stage clustering result with ART1 model and then merge similar clusters in the second stage. Our strategy is a mimic process of manual disambiguation and need not to predict the number of clusters, which makes it competent for the disambiguation task. Experimental results show that, in comparison with the agglomerative clustering method, our strategy improves the performance by respectively 0.92% and 5.00% on two kinds of name recognition results.

Word sense disambiguation based on rough set

A sense feature system (SFS) is first automatically constructed from the text corpora to structurize the textural information. WSD rules are then extracted from SFS according to their certainty factors and are applied to disambiguate the senses of polysemous words. The entropy of a deterministic rough prediction is used to measure the decision quality of a rule set. Finally, the back off rule smoothing method is further designed to improve the performance of a WSD model. In the experiments, a mean rate of correction achieved during experiments for WSD in the case of rule smoothing is 0.92.

Chinese word sense disambiguation based on neural networks

The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to (-M,+N).The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of Mand Naffect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.

Word Sense Disambiguation in Information Retrieval

The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers.

Word Sense Disambiguation Model with a Cache-Like Memory Module

Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long short term memory(LSTM),which is able to better capture sequential and syntactic features of text.However,this method neglects the dependencies among instances,such as their context semantic similarities.To solve this problem,we proposed a novel WSD model by introducing a cache-like memory module to capture the semantic dependencies among instances for WSD.Extensive evaluations on standard datasets demonstrate the superiority of the proposed model over various baselines.

An Improved Name Disambiguation Method Based on Atom Cluster

An improved name disambiguation method based on atom cluster. Aiming at the method of character-related properties of similarity based on information extraction depends on the character information, a new name disambiguation method is proposed, and improved k-means algorism for name disambiguation is proposed in this paper. The cluster analysis cluster is introduced to the name disambiguation process. Experiment results show that the proposed method having the high implementation efficiency and can distinguish the different people with the same name.

Name Disambiguation Method Based on Attribute Match and Link Analysis

A name disambiguation method is proposed based on attribute match and link analysis applying in the field of insurance. Aiming at the former name disambiguation methods such as text clustering method needs to be considered in a lot of useless words, a new name disambiguation method is advanced. Firstly, the same attribute matching is applied, merging the identity of a successful match, secondly, the link analysis is used, structural analysis of customers network is analyzed, Finally, the same cooperating information is merged. Experiment results show that the proposed method can realize name disambiguation successfully.

Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation

Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. We experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. We also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%.

Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation

Word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation

In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real tasks.Furthermore,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on mini-batches.In this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination ability.On the one hand,we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes.On the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantlyoutperforms the SOTA methods.

Towards Effective Author Name Disambiguation by Hybrid Attention

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic publications.To tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so far.To fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion feature.Each hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature extractor.Additionally,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector space.Experimental results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Sentence Boundary Disambiguation(SBD)is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning(DL)and pretraining language models.Tibetan punctuation marks may involve ambiguity about the sentences’beginnings and endings.Hence,the ambiguous punctuation marks must be distinguished,and the sentence structure must be correctly encoded in language models.This study proposed a component-level Tibetan SBD approach based on the DL model.The models can reduce the error amplification caused by word segmentation and part-of-speech tagging.Although most SBD methods have only considered text on the left side of punctuation marks,this study considers the text on both sides.In this study,465669 Tibetan sentences are adopted,and a Bidirectional Long Short-Term Memory(Bi-LSTM)model is used to perform SBD.The experimental results show that the F1-score of the Bi-LSTM model reached 96%,the most efficient among the six models.Experiments are performed on low-resource languages such as Turkish and Romanian,and high-resource languages such as English and German,to verify the models’generalization.

Incorporating multi-kernel function and Internet verification for Chinese person name disambiguation

The study on person name disambiguation aims to identify different entities with the same person name through document linking to different entities. The traditional disambiguation approach makes use of words in documents as features to distinguish different entities. Due to the lack of use of word order as a feature and the limited use of external knowledge, the traditional approach has performance limitations. This paper presents an approach for named entity disambiguation through entity linking based on a multi- kernel function and Internet verification to improve Chinese person name disambiguation. The proposed approach extends a linear kernel that uses in-document word features by adding a string kernel to construct a multi-kernel function. This multi-kernel can then calculate the similarities between an input document and the entity descriptions in a named per- son knowledge base to form a ranked list of candidates to different entities. Furthermore, Internet search results based on keywords extracted from the input document and entity descriptions in the knowledge base are used to train classifiers for verification. The evaluations on CIPS-SIGHAN 2012 person name disambiguation bakeoff dataset show that the use of word orders and Internet knowledge through a multi-kernel function can improve both precision and recall and our system has achieved state-of-the-art performance.

Topological Features Based Entity Disambiguation

This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.

An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems

Entity recognition and disambiguation （ERD） is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework （EUEF）, which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.