scholarly journals GENERATING TRAINING DATA FOR WORD SENSE DISAMBIGUATION IN RUSSIAN

2020 ◽  
Author(s):  
A. S. Bolshina ◽  
◽  
N. V. Loukachevitch ◽  
Keyword(s):  
Nearest Neighbor ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Russian Language ◽  
Training Data ◽  
Word Sense ◽  
Training Samples ◽  
Sense Disambiguation ◽  
News Corpus ◽  
Neighbor Classification

The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create. For the Russian language there is no sense-tagged resource of the size sufficient to train supervised word sense disambiguation algorithms. In our work we describe an approach that is used to create an automatically labelled collection based on the monosemous relatives (related unambiguous entries). The main contribution of our work is that we extracted monosemous relatives that can be located at relatively long distances from a target ambiguous word and ranked them according to the similarity measure to the target sense. The selected candidates are then used to extract training samples from the news corpus. We evaluated word sense disambiguation models based on a nearest neighbor classification on BERT and ELMo embeddings. Our work relies on the Russian wordnet RuWordNet.

scholarly journals deepBioWSD: effective deep neural word sense disambiguation of biomedical text data

2019 ◽  
Vol 26 (5) ◽  
pp. 438-446 ◽  
Author(s):  
Ahmad Pesaranghader ◽  
Stan Matwin ◽  
Marina Sokolova ◽  
Ali Pesaranghader
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Word Sense Disambiguation ◽  
Training Data ◽  
Biomedical Text ◽  
Word Sense ◽  
Vocabulary Size ◽  
Unified Medical Language System ◽  
Knowledge Based ◽  
Sense Disambiguation

Abstract Objective In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an unattainable goal in medical informatics. To alleviate this need, a single model that shares statistical strength across all instances and scales well with the vocabulary size is desirable. Materials and Methods Built on recent advances in deep learning, our deepBioWSD model leverages 1 single bidirectional long short-term memory network that makes sense prediction for any ambiguous term. In the model, first, the Unified Medical Language System sense embeddings will be computed using their text definitions; and then, after initializing the network with these embeddings, it will be trained on all (available) training data collectively. This method also considers a novel technique for automatic collection of training data from PubMed to (pre)train the network in an unsupervised manner. Results We use the MSH WSD dataset to compare WSD algorithms, with macro and micro accuracies employed as evaluation metrics. deepBioWSD outperforms existing models in biomedical text WSD by achieving the state-of-the-art performance of 96.82% for macro accuracy. Conclusions Apart from the disambiguation improvement and unsupervised training, deepBioWSD depends on considerably less number of expert-labeled data as it learns the target and the context terms jointly. These merit deepBioWSD to be conveniently deployable in real-time biomedical applications.

scholarly journals A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Language Processing ◽  
Fundamental Problem ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Features ◽  
Word Sense ◽  
Semantic Association ◽  
Data Set ◽  
Novel Approach ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.

Evaluating sense disambiguation across diverse parameter spaces

2002 ◽  
Vol 8 (4) ◽  
pp. 293-310 ◽  
Author(s):  
DAVID YAROWSKY ◽  
RADU FLORIAN
Keyword(s):  
Word Sense Disambiguation ◽  
Model Performance ◽  
Training Data ◽  
Target Language ◽  
Word Sense ◽  
Parameter Spaces ◽  
Diverse Range ◽  
Part Of Speech ◽  
Sense Disambiguation ◽  
Training Examples

This paper presents a comprehensive empirical exploration and evaluation of a diverse range of data characteristics which influence word sense disambiguation performance. It focuses on a set of six core supervised algorithms, including three variants of Bayesian classifiers, a cosine model, non-hierarchical decision lists, and an extension of the transformation-based learning model. Performance is investigated in detail with respect to the following parameters: (a) target language (English, Spanish, Swedish and Basque); (b) part of speech; (c) sense granularity; (d) inclusion and exclusion of major feature classes; (e) variable context width (further broken down by part-of-speech of keyword); (f) number of training examples; (g) baseline probability of the most likely sense; (h) sense distributional entropy; (i) number of senses per keyword; (j) divergence between training and test data; (k) degree of (artificially introduced) noise in the training data; (l) the effectiveness of an algorithm's confidence rankings; and (m) a full keyword breakdown of the performance of each algorithm. The paper concludes with a brief analysis of similarities, differences, strengths and weaknesses of the algorithms and a hierarchical clustering of these algorithms based on agreement of sense classification behavior. Collectively, the paper constitutes the most comprehensive survey of evaluation measures and tests yet applied to sense disambiguation algorithms. And it does so over a diverse range of supervised algorithms, languages and parameter spaces in single unified experimental framework.

scholarly journals Detection and Analysis of Stress using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 335-342
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Text Messages ◽  
Word Sense ◽  
Severe Problem ◽  
Sense Disambiguation

Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter.This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength depends on the nature of the tweets.In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or "senses." In the pre-processing partof-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better resultPrecision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets. Keywords: Stress Detection, Data Mining, TensiStrength, word sense disambiguation.

scholarly journals Analysis and Evaluation of Language Models for Word Sense Disambiguation

2021 ◽  
pp. 1-55
Author(s):  
Daniel Loureiro ◽  
Kiamehr Rezaee ◽  
Mohammad Taher Pilehvar ◽  
Jose Camacho-Collados
Keyword(s):  
Feature Extraction ◽  
Word Sense Disambiguation ◽  
Language Model ◽  
Training Data ◽  
Fine Tuning ◽  
Language Models ◽  
Coarse Grained ◽  
Word Sense ◽  
Sense Disambiguation ◽  
High Level

Abstract Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model based WSD strategies, i.e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.

scholarly journals Exemplification Modeling: Can You Give Me an Example, Please?

2021 ◽  
Author(s):  
Edoardo Barba ◽  
Luigi Procopio ◽  
Caterina Lacerra ◽  
Tommaso Pasini ◽  
Roberto Navigli
Keyword(s):  
Gold Standard ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Full Range ◽  
Training Data ◽  
Training Procedure ◽  
Word Sense ◽  
The Novel ◽  
Current State ◽  
Sense Disambiguation

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.

scholarly journals Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation

2021 ◽  
Vol 11 (6) ◽  
pp. 2488
Author(s):  
Jinfeng Cheng ◽  
Weiqin Tong ◽  
Weian Yan
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Semantic Representation ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Great Contribution ◽  
Specific Context ◽  
The Core ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the context and sense glosses of the target polysemy independently using encoders with the same structure. To obtain a better presentation in each encoder, we leverage the capsule network to capture different important information contained in multi-head attention. We finally choose the gloss representation closest to the context representation of the target word as its correct sense. We do experiments on English all-words WSD task. Experimental results show that our method achieves good performance, especially having an inspiring effect on disambiguating words of rare senses.

Co-occurrence graph-based context adaptation: a new unsupervised approach to word sense disambiguation

2020 ◽  
Author(s):  
Saeed Rahmani ◽  
Seyed Mostafa Fakhrahmad ◽  
Mohammad Hadi Sadreddini
Keyword(s):  
Text Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Experimental Results ◽  
Word Sense ◽  
Structure And Properties ◽  
Similarity Functions ◽  
Challenging Tasks ◽  
Sense Disambiguation ◽  
Unsupervised Approach

Abstract Word sense disambiguation (WSD) is the task of selecting correct sense for an ambiguous word in its context. Since WSD is one of the most challenging tasks in various text processing systems, improving its accuracy can be very beneficial. In this article, we propose a new unsupervised method based on co-occurrence graph created by monolingual corpus without any dependency on the structure and properties of the language itself. In the proposed method, the context of an ambiguous word is represented as a sub-graph extracted from a large word co-occurrence graph built based on a corpus. Most of the words are connected in this graph. To clarify the exact sense of an ambiguous word, its senses and relations are added to the context graph, and various similarity functions are employed based on the senses and context graph. In the disambiguation process, we select senses with highest similarity to the context graph. As opposite to other WSD methods, the proposed method does not use any language-dependent resources (e.g. WordNet) and it just uses a monolingual corpus. Therefore, the proposed method can be employed for other languages. Moreover, by increasing the size of corpus, it is possible to enhance the accuracy of WSD. Experimental results on English and Persian datasets show that the proposed method is competitive with existing supervised and unsupervised WSD approaches.

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