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The problem of resolving the uncertainties associated with homonymy for the Chechen language has become especially relevant after the creation of speech synthesis systems. The main disadvantage of speech synthesizers in the Chechen language are errors in reading homograph words that differ in the length / brevity of vowels — the longitude of such sounds is not displayed in any way when writing. The reproduction of diphthongs, which are indicated on the letter in the same way as monophthongs close to them in sound, causes problems. To improve the quality of synthesized speech in the Chechen language, an automatic homograph recognition program is needed. To solve this problem, the article considers the task of eliminating the ambiguity of the meaning of the words WSD (Word Sense Disambiguation). Algorithmic (supervised) methods based on a pre-marked database have been selected for the Chechen language. These methods are the most common solutions for eliminating the ambiguity of the meaning of words. The implementation of such methods is possible in the presence of large marked-up corpora that are inaccessible to most languages of the world including Chechen. The Chechen language belongs to low-resource languages for which the optimal approach from the point of view of saving labor and time resources is a semi-controlled hybrid method of homograph recognition based on the use of algorithmic and statistical methods. The algorithm created by the authors for recognizing homographs by six adjacent words in a sentence is presented. The method is implemented as a program. Preliminary preparation of the initial data for the operation of the algorithm includes marking of proposals by the values of homographs performed “manually”. The results of the program were evaluated using generally recognized accuracy metrics and amounted to F1 — 39 %, Accuracy — 45 %. A comparative analysis of the data obtained with the results of other methods and models showed that the accuracy of the algorithm presented in this article is closest to the results of the accuracy of algorithms based on the Lesk method. Using Lesk method for English, the results of F1 accuracy were obtained — 41.1 % (simple Lesk) and 51.1 % (extended Lesk). Methods using neural network algorithms provide higher WSD accuracy rates for most languages; however, their implementation requires large data bodies, which is not always available for low-resource languages, including Chechen.

According to natural language processing experts, there are numerous ambiguous words in languages. Without automated word meaning disambiguation for any language, the development of natural language processing technologies such as information extraction, information retrieval, machine translation, and others are still challenging task. Therfore, this paper presents the development of a word sense disambiguation model for duplicate alphabet words for the Ge'ez language using corpus-based methods. Because there is no wordNet or public dataset for the Ge'ez language, 1010 samples of ambiguous words were gathered. Afterwards, the words were preprocessed and the text was vectorized using bag of words, Term Frequency-Inverse Document Frequency, and word embeddings such as word2vec and fastText. The vectorized texts are then analysed using the supervised machine learning algorithms such Naive Bayes, decision trees, random forests, K-nearest neighbor, linear support vector machine, and logistic regression. Bag of words paired with random forests outperformed all other combinations, with an accuracy of 99.52%. However, when Deep learning algorithms such as Deep neural network and Long Short-Term memory were used for the same dataset, a 100% accuracy was achieved.

The environmental load factor in commonly used offshore platforms design code, API RP-2A (American Petroleum Institute - Recommended Practice 2A), is developed based on the environmental conditions of American waters, especially the Gulf of Mexico, which have relatively extreme environmental conditions when compared to Indonesian waters. Case studies were conducted to determine environmental load factors in Indonesian waters, particularly the North Java Sea and Makassar Strait, categorized as shallow seas. This analysis was carried out on the performance criteria of pushover failure. In this study, the base shear was analyzed to describe the strength of the structure in the form of a collapse base shear (CBS) and the load in the form of a wave base shear (WBS). CBS was obtained through pushover analysis with yield strength randomness. WBS was obtained through in-place analysis with wave height randomness. This concept was applied to the structure of the Monopod and Braced Monopod types of offshore platforms located in the North of Java Sea and Makassar Strait waters which had been optimized for the WSD and LRFD design methods. The reliability of the structure was analyzed based on the CBS and WBS values using the First Order Reliability Method (FORM) II. The reliability analysis results were in the form of a reliability index (ß). North Java Sea gives a reliability index in the range from 3.58 to 4.38 for every design criteria. While Makassar Strait gives a reliability index in the range from 3.17 to 3.54 for every design criteria. With a high target safety level for the North Java Sea location, a 1.10 environmental load factor is recommended for further offshore structure design. But, for the Makassar Strait location, more studies need to be done to get better environmental load factor recommendations. [ABSTRACT FROM AUTHOR]

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash–Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development. [ABSTRACT FROM AUTHOR]

The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats. [ABSTRACT FROM AUTHOR]

This paper presents ACCWSI (Attentive Context Clustering WSI), a method for Word Sense Induction, suitable for languages with limited resources. Pretrained on a small corpus and given an ambiguous word (a query word) and a set of excerpts that contain it, ACCWSI uses an attention mechanism for generating context-aware embeddings, distinguishing between the different senses assigned to the query word. These embeddings are then clustered to provide groups of main common uses of the query word. We show that ACCWSI performs well on the SemEval-2 2010 WSI task. ACCWSI also demonstrates practical applicability for shedding light on the meanings of ambiguous words in ancient languages, such as Classical Hebrew and Akkadian. In the near future, we intend to turn ACCWSI into a practical tool for linguists and historians.

Natural language processing is associated with human-computer interaction, where several challenges require natural language understanding. The Word sense disambiguation problem comprises the computational assignment of meaning to a word according to a specific context in which it occurs. There are numerous natural language processing applications, such as machine translation, information retrieval, and information extraction, which require this task which takes place at the semantic level. To solve this problem unsupervised computation proposals can be effective since they have been successfully used for many real-world optimization problems. In this paper, we propose to solve the word sense disambiguation problem using the cuckoo search algorithm in the Assamese language. We illustrate the performance of our algorithm by carrying out experiments on an Assamese corpus. And comparing them against an unsupervised genetic algorithm that is implemented in the Assamese language. Results of the experiment show that the cuckoo algorithm can achieve more precision, recall and F-measure, attaining 87.5, 84, and 85.71 percentages respectively. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD. [ABSTRACT FROM AUTHOR]

It is evident that the traditional hard matching of a fixed-length template cannot satisfy the nearly indefinite variations in natural language. This issue mainly results from three major problems of the traditional matching mode: 1) in matching with a short template, the context of natural language cannot be effectively captured; 2) in matching with a long template, serious data sparsity will lead to a low success rate of template matching (i.e., low recall); and 3) due to a lack of flexible matching ability, traditional hard matching is more prone to failure. Therefore, this paper proposed a novel method of stretchable matching of the semantic template (SMOST) to deal with the above problems. We have applied this method to word sense disambiguation in the natural language processing field. In the same case of using only the SemCor corpus, the result of our system is very close to the best result of existing systems, which shows the effectiveness of new proposed method. [ABSTRACT FROM AUTHOR]

The article presents the summary of a dataset related to the risks factors of white spot disease (WSD) of farmed shrimp (Penaeus monodon) in Khulna, Bagerhat and Satkhira districts of Bangladesh. This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools were applied to get the conceptual framework for data collection regarding lists of farmers and the variables of the risk factors of WSD. In the second step, sampling of farmers, google featured questionnaire development, and mobile phone-assisted survey were carried out. The total surveyed farms were 233 consisting of 21 and 212 semi-intensive and extensive farms, respectively. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains some basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article.

Polysemic acronyms are very common in the field of biomedicine. These acronyms have different senses in different contexts. The ambiguity of acronyms may cause significant negative impact on the understanding of the full text by machine learning. To address the disambiguation of acronyms in the biomedical domain, most associated studies are based on methods using word-level contextual features. These methods require abundant relevant external resources for model training, and the accuracy of their disambiguation of acronyms may decrease greatly upon the lack of external resources. In this study, disambiguation of biomedical acronyms was investigated on the basis of the character-level feature model to realize the disambiguation of biomedical acronyms with largely limited external corpora. First, sentences containing ambiguous acronyms were extracted through retrieval and the feature vector of the context were initialized by using the character-level features. Second, these initial vectors were input into the bidirectional long shortterm memory neutral network model for training. Finally, the disambiguation of acronyms was realized by the outputs of the neutral network model through the Softmax classification approach. The results of acronym disambiguation based on character-level feature model were also compared with those based on word-level feature models. Results demonstrate that the average accuracy of the character-level feature neutral network algorithm reaches 85.82% on the dataset of 106 common biomedical acronyms. Thus, the character-level feature neutral network algorithm is superior to the traditional methods, which use a large number of external resources. This study confirms that the disambiguation method based on character-level features is applicable to the disambiguation of biomedical acronyms under limited relevant data. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

The problem of word sense disambiguation (WSD) is considered in the article. Set of synonyms (synsets) and sentences with these synonyms are taken. It is necessary to automatically select the meaning of the word in the sentence. 1285 sentences were tagged by experts, namely, one of the dictionary meanings was selected by experts for target words. To solve the WSD problem, an algorithm based on a new method of vector-word contexts proximity calculation is proposed. A preliminary epsilon-filtering of words is performed, both in the sentence and in the set of synonyms, in order to achieve higher accuracy. An extensive program of experiments was carried out. Four algorithms are implemented, including the new algorithm. Experiments have shown that in some cases the new algorithm produces better results. The developed software and the tagged corpus have an open license and are available online. Wiktionary and Wikisource are used. A brief description of this work can be viewed as slides (https://goo.gl/9ak6Gt). A video lecture in Russian about this research is available online (https://youtu.be/-DLmRkepf58).

Seeing the dynamic user demands the libraries are changing their approach to the user services. The web-scale discovery (WSD) service is the latest attempt in this direction. There are several players in the marketplace providing solutions for WSD with products having basic features and subtle features as well. As more players are entering the marketplace, it becomes challenging to select appropriate WSD system. It is also paramount for the library managers to be aware of the myriad range of features and underlying technology of WSD. This understanding will be for making informed purchase decisions. This paper attempts to explain in detail the components of a typical WSD system. Further, the paper evaluates the features of the Big 4's in WSD. The paper concludes by discussing some of the parameters to consider while evaluating the WSD system. [ABSTRACT FROM AUTHOR]

The contact of diesel fuel with engine subsystems demands a good wear resistance. Lubricity is an important feature for integrity of injection system and the sulphur composites are primarily responsible for lubrication of the injector nozzle. Biodiesel is responsible for partially restoring the lubricity of diesel fuel that presents low levels of sulphur composites and, furthermore, it causes less pollution than diesel fuel. The lubricity is measured through the wear scar diameter following the ASTM D 975 standards. However, the friction and wear with light loads of micro/nanocomponents are highly dependent on surface interactions that can be evaluated by microscopy techniques. This study aimed to measure and to analyze the biodiesel lubricity and their blends (B5, B20) with diesel by observing the wear scars of discs using the scanning electronic microscopy (SEM), atomic force microscopy (AFM) and micro roughness techniques. The fuels performance was evaluated using HFRR tribometer. The tests conditions were based on standard ADTM D-6079-04. The coefficient of friction was measure during the test. After the test, the worn ball and disc were analyzed by SEM, AFM and profilometer. The results showed that the addition of biodiesel in diesel improve the tribological performance of fuel. Also, the just WSD value is not sufficient to evaluate the lubrication ability of a fuel. Analysis of the worn disc surfaces proved to be compatible with WSD number and also more sensitive to these kinds of fuels, showing mainly the form and intensity of the wear.

We explore human judgments on how well individual patterns of 29 target verbs from the Pattern Dictionary of English Verbs describe their random KWICs. We focus on cases where more than one pattern is judged as highly appropriate for a given KWIC and seek to estimate the effect of event participants (arguments) being denotatively similar in two patterns, considering all pair combinations in a given lemma. We compare this effect to the effect of several contextual features of the KWICs, the effect of paired PDEV implicatures implying each other, and the effect of belonging to a given lemma. We show that the lemma effect is still stronger than any feature going across lemmas we have examined so far, so that each verb appears to be a little universe in its own right. [ABSTRACT FROM AUTHOR]

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In many cases, a single presentation of a term may carry multiple meanings. Wiktionary provides a way for viewing the meanings of the different terms it stores in the form of senses. It also provides semantic relations. However, Wiktionary, in its current form, contains semantic relations linking Wiktionary entries at the term level. Links for semantic relations connecting entries at the word sense level do not currently exist in Wiktionary. In this paper, we propose a novel method for generating a new type of links for semantic relations within Wiktionary. This is effectively applied to aligning the source words senses for semantic relations in Wiktionary with their corresponding target word senses. We use surface-level features that rely only on the structure and content of Wiktionary for completing this task without the aid of any external lexical or knowledge bases. We present the details of the method and how it was implemented. Additionally, we describe the evaluations that we performed and illustrate the competitive results we obtained, especially when compared to other systems. Our findings indicate that our system outperforms the baselines and performs similar to state-of-art systems without requiring access to external online resources or training data to run. [ABSTRACT FROM AUTHOR]

The term of word sense disambiguation, WSD, is introduced in the context of text document processing. A knowledge based approach is conducted using WordNet lexical ontology, describing its structure and components used for the process of identification of context related senses of each polysemy words. The principal distance measures using the graph associated to WordNet are presented, analyzing their advantages and disadvantages. A general model for aggregation of distances and probabilities is proposed and implemented in an application in order to detect the context senses of each word. For the non-existing words from WordNet, a similarity measure is used based on probabilities of co-occurrences. The module of WSD is proposed for integration in the step of processing documents such as supervised and unsupervised classification in order to maximize the correctness of the classification. Future work is related to the implementation of different domain oriented ontologies.

Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical Resources Lexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed.

Correct and automatical semantic analysis has always been one of major goals in natural language understanding. However, due to the difficulties in deep semantic analysis, at present, the mainstream studies of semantic analysis are focused on semantic role labeling (SRL) and word sense disambiguation (WSD). Nowadays, these two issues are mostly considered as separate tasks. However, this approach ignores possible dependencies between them. In order to address the issue, an integrative semantic analysis model based on synergetic neural network (SNN) is proposed in this paper, which can easily express useful logic constraints between SRL and WSD. The semantic analysis process can be viewed as the competition process of semantic order parameters. The strongest order parameter will win by competition and desired semantic patterns will be recognized. There are three main innovations in this paper. First, an integrative semantic analysis model is proposed that jointly models word sense disambiguationand semantic role labeling. Second, integrative order parameter is reconstructed to reflect the relation among semantic patterns. Finally, integrative network parameters and integrative evolution equation are reconstructed, which can reflect the relationship of guiding and driving each other between word sense and semantic roles. The experiment results on OntoNotes 2.0 corpus shows the integrative method in this paper has a higher performance for semantic role labeling and word sense disambiguation, and provides a good practicability and a promising future for other natural language processing tasks. [ABSTRACT FROM AUTHOR]