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.

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 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.

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.

Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity ranking.It is of great significance to some NLP(natural language processing)tasks,such as question answering.Unlike English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain scenarios.In Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily nested.In addition,the meanings of the words that make up industrial entities are sometimes ambiguous.Their semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact meanings.Therefore,we propose two schemes to achieve better Chinese entity linking.First,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting noise.Then,we enhance the corresponding candidate entity ranking mechanism by introducing sense embedding.Considering the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with context.We test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general scenarios.We confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus

We present and analyze an unsupervised method for Word Sense Disambiguation（WSD）.Our work is based on the method presented by McCarthy et al.in 2004 for finding the predominant sense of each word in the entire corpus.Their maximization algorithm allows weighted terms（similar words） from a distributional thesaurus to accumulate a score for each ambiguous word sense,i.e.,the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word.This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus.In the method of McCarthy et al.,every occurrence of the ambiguous word uses the same thesaurus,regardless of the context where the ambiguous word occurs.Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word.We obtain a top precision of 77.54%of accuracy versus 67.10%of the original method tested on SemCor.We also analyze the effect of the number of weighted terms in the tasks of finding the Most Precuent Sense（MFS） and WSD,and experiment with several corpora for building the Word Space Model.