Syntactic Similarity Research Articles

Determination of Professional Competencies Using an Alignment Algorithm of Academic Profiles and Job Advertisements, Based on Competence Thesauri and Similarity Measures

Describing the competencies required by a profession is essential for aligning online profiles of job seekers and job advertisements. Comparing the competencies described within each context has typically not be done, which has generated a complete disconnect in language between them. This work presents an approach for the alignment of online profiles and job advertisements, according to knowledge and skills, using measures of lexical, syntactic and taxonomic similarity. In addition, we use a ranking that allows the alignment of the profiles to the topics of a thesaurus that define competencies. The results are promising, because the combination of the measures of similarity with the alignment with thesauri of competencies offers robustness to the process of generation of professional competence descriptions. This combination allows dealing with the common problems of synonymy, homonymy, hypernymy/hyponymy and meronymy of the terms in Spanish. This research uses natural language processing to offer a novel approach for assessing the match of the competencies described by the applicants and by the employers, even if they use different terminology. The resulting approach, while developed in Spanish for computer science jobs, can be extended to other languages and domains, such is the case of recruitment, where it will contribute to the creation of better tools that give feedback to job seekers about how to best align their competencies with job opportunities.

Who is “Profaned?” Rabbinic and Samaritan (Re)formulations of Leviticus 21:9

Abstract Second Temple, rabbinic, and Samaritan sources preserve a variety of interpretations and (re)formulations of Leviticus 21:9. The pivotal issue informing the various approaches to this verse is the identity of the person “profaned” by the conduct of the priest’s daughter; specifically, is it the daughter, herself, or her father who is (directly) affected? The present essay examines various rabbinic and Samaritan interpretations of this verse, noting the exegetical (i.e., morphological and syntactic) similarities and differences obtaining among these positions. Especial attention is devoted to the formulation of Targum Onqelos, for which two explanations are proposed, and the similar exegetical features reflected in Samaritan renderings of this passage. It is demonstrated that, like Targum Onqelos and additional rabbinic/Jewish targumic sources, Samaritan sources indicate that some Samaritan students of Leviticus understood Lev 21:9 to mean that it is the daughter, herself, who is profaned by her conduct.

Framework To Approximate Label Matching For Automatic Assessment Of Use-Case Diagram

E-learning plays a significant role in educating large number of students. In the delivery of e-learning material, automatic e-assessment has been applied only to some extent in the case of free response answers in highly technical diagrams in domains like software engineering, electronics, etc., where there is a great scope of imagination and wide variations in answers. Therefore, the automatic assessment of diagrammatic answers is a challenging task. In this article, algorithms that compute the syntactic and semantic similarities of nodes to fulfill the objective of automatic assessment of use-case diagrams are described. To illustrate the performance of these algorithms, students' use-case diagrams are matched with model use-case diagram. Results from 13,749 labels of 445 student answers based on 14 different scenarios are analyzed to provide quantitative and qualitative feedback. No comparable study has been reported by any other label matching algorithms before in the research literature.

Towards a unified framework for string similarity joins

A similarity join aims to find all similar pairs between two collections of records. Established algorithms utilise different similarity measures, either syntactic or semantic, to quantify the similarity between two records. However, when records are similar in forms of a mixture of syntactic and semantic relations, utilising a single measure becomes inadequate to disclose the real similarity between records, and hence unable to obtain high-quality join results. In this paper, we study a unified framework to find similar records by combining multiple similarity measures. To achieve this goal, we first develop a new similarity framework that unifies the existing three kinds of similarity measures simultaneously, including syntactic (typographic) similarity, synonym-based similarity, and taxonomy-based similarity. We then theoretically prove that finding the maximum unified similarity between two strings is generally NP -hard, and furthermore develop an approximate algorithm which runs in polynomial time with a non-trivial approximation guarantee. To support efficient string joins based on our unified similarity measure, we adopt the filter-and-verification framework and propose a new signature structure, called pebble , which can be simultaneously adapted to handle multiple similarity measures. The salient feature of our approach is that, it can judiciously select the best pebble signatures and the overlap thresholds to maximise the filtering power. Extensive experiments show that our methods are capable of finding similar records having mixed types of similarity relations, while exhibiting high efficiency and scalability for similarity joins. The implementation can be downloaded at https://github.com/HY-UDBMS/AU-Join.

Confusion2Vec: towards enriching vector space word representations with representational ambiguities.

Word vector representations are a crucial part of natural language processing (NLP) and human computer interaction. In this paper, we propose a novel word vector representation, Confusion2Vec, motivated from the human speech production and perception that encodes representational ambiguity. Humans employ both acoustic similarity cues and contextual cues to decode information and we focus on a model that incorporates both sources of information. The representational ambiguity of acoustics, which manifests itself in word confusions, is often resolved by both humans and machines through contextual cues. A range of representational ambiguities can emerge in various domains further to acoustic perception, such as morphological transformations, word segmentation, paraphrasing for NLP tasks like machine translation, etc. In this work, we present a case study in application to automatic speech recognition (ASR) task, where the word representational ambiguities/confusions are related to acoustic similarity. We present several techniques to train an acoustic perceptual similarity representation ambiguity. We term this Confusion2Vec and learn on unsupervised-generated data from ASR confusion networks or lattice-like structures. Appropriate evaluations for the Confusion2Vec are formulated for gauging acoustic similarity in addition to semantic–syntactic and word similarity evaluations. The Confusion2Vec is able to model word confusions efficiently, without compromising on the semantic-syntactic word relations, thus effectively enriching the word vector space with extra task relevant ambiguity information. We provide an intuitive exploration of the two-dimensional Confusion2Vec space using principal component analysis of the embedding and relate to semantic relationships, syntactic relationships, and acoustic relationships. We show through this that the new space preserves the semantic/syntactic relationships while robustly encoding acoustic similarities. The potential of the new vector representation and its ability in the utilization of uncertainty information associated with the lattice is demonstrated through small examples relating to the task of ASR error correction.

What Do Language Representations Really Represent?

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.

Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet

Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the context and sense definitions provided by a large lexical database of English, WordNet. In this database, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms interlinked through conceptual semantics and lexicon relations. Traditional unsupervised approaches compute similarity by counting overlapping words between the context and sense definitions which must match exactly. Similarity should compute based on how words are related rather than overlapping by representing the context and sense definitions on a vector space model and analyzing distributional semantic relationships among them using latent semantic analysis (LSA). When a corpus of text becomes more massive, LSA consumes much more memory and is not flexible to train a huge corpus of text. A word-embedding approach has an advantage in this issue. Word2vec is a popular word-embedding approach that represents words on a fix-sized vector space model through either the skip-gram or continuous bag-of-words (CBOW) model. Word2vec is also effectively capturing semantic and syntactic word similarities from a huge corpus of text better than LSA. Our method used Word2vec to construct a context sentence vector, and sense definition vectors then give each word sense a score using cosine similarity to compute the similarity between those sentence vectors. The sense definition also expanded with sense relations retrieved from WordNet. If the score is not higher than a specific threshold, the score will be combined with the probability of that sense distribution learned from a large sense-tagged corpus, SEMCOR. The possible answer senses can be obtained from high scores. Our method shows that the result (50.9% or 48.7% without the probability of sense distribution) is higher than the baselines (i.e., original, simplified, adapted and LSA Lesk) and outperforms many unsupervised systems participating in the SENSEVAL-3 English lexical sample task.

Framework for syntactic string similarity measures

Similarity measure is an essential component of information retrieval, document clustering, text summarization, and question answering, among others. In this paper, we introduce a general framework of syntactic similarity measures for matching short text. We thoroughly analyze the measures by dividing them into three components: character-level similarity, string segmentation, and matching technique. Soft variants of the measures are also introduced. With the help of two existing toolkits (SecondString and SimMetric), we provide an open-source Java toolkit of the proposed framework, which integrates the individual components together so that completely new combinations can be created. Experimental results reveal that the performance of the similarity measures depends on the type of the dataset. For well-maintained dataset, using a token-level measure is important but the basic (crisp) variant is usually enough. For uncontrolled dataset where typing errors are expected, the soft variants of the token-level measures are necessary. Among all tested measures, a soft token-level measure that combines set matching and q-grams at the character level perform best. A gap between human perception and syntactic measures still remains due to lacking semantic analysis.

Albanian Text Classification: Bag of Words Model and Word Analogies

Abstract Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.

Linguistic Intercomprehension in Vocational Translation Training (The Case of English, Spanish, Russian)

According to a common adage, translation is not about translating words but about translating meanings. One and the same meaning, thus, in different languages can be rendered by different linguistic means. Mastering the art of translation often means juggling a variety of these linguistic means in two or more languages. Traditionally, translators for international organizations have worked from a foreign language (so- called languages B/C) into their native language (so-called language A). Today, however, in the increasingly global world, translators are more often than not required to be able to work into at least one, usually two, foreign languages. Moreover, sometimes translators may find themselves tasked with working from language C into language B, i.e. from one foreign language into another, leaving their native language out of the equation altogether. Schools of translation do not normally offer this kind of training. To respond to the growing professional demand, however, there emerged a need for research and teaching strategy development for such a course. This paper outlines a sequence of teaching activities for a group of Russian speaking students majoring in International Media and Communication Studies at MGIMO-University (Moscow State Institute of International Relations). The students, all of whom are native Russian speakers, have reached CEFR (Common European Framework of Reference) C1 level in English and CEFR B2 in Spanish, and are currently taking two professionally-oriented translation courses, from English into Russian and from Spanish into Russian. The paper puts forward a lesson plan where the students are taught translation from Spanish (language C) into English (language B). The activities are aimed at identifying syntactical and lexical differences and similarities between English and Spanish, as well as conducting a comparative analysis of the students’ native language, Russian. The paper argues that translation from one foreign language into another is facilitated by linguistic intercomprehension, a field in linguistic research that emerged relatively recently and has so far mainly focused on intercomprehension in language teaching.

Label Comparison Analysis Using Hybrid Similarity for Labeling Systems of Indonesian University Websites (English Menu Version)

Labeling systems are an important component in designing a university website. In order to avoid misinformation, website developers need to design a website labeling system that can represent information that a website owner wants to deliver to the users of her website. One way of designing a labeling system is by comparing and studying the labeling system used on competitor’s websites. Syntactic similarity is usually used for comparing the websites’ labels. However, it has a limitation that can only calculate similarities based on strings only, so that the possibility of two compared labels having the same meaning will be considered as different labels. To provide the meaning of the label, the comparison using semantic similarity is used. However, semantic similarity has limitations such as not being able to process two words or more and possibly having no path exists between two word senses. Therefore, this research proposes a hybrid similarity where the highest result of syntactic and semantic similarity comparison between two labels is combined and able to cover the limitation of two methods. The hybrid similarity shows the results of possibility of higher score between combined syntactic and semantic similarity score with suggestion on the list label (based on semantic result). The expert suggest output of hybrid list label to help developer create and reviewing existing label, and then participant who answers the questionnaires 82.16% agree with label that show the acceptance of the label. The list label based on hybrid score can help and assisting the web developer to create new labeling systems for a new website. It is also help developer to improving an existing website’s labeling systems. Keywords: university website, labeling systems, syntactic similarity, semantic similarity, hybrid similarity, Levensthein Distance, Wu and Palmer.

Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification

The goal of this paper is to reduce the classification (inference) complexity of tree ensembles by choosing a single representative model out of ensemble of multiple decision-tree models. We compute the similarity between different models in the ensemble and choose the model, which is most similar to others as the best representative of the entire dataset. The similarity-based approach is implemented with three different similarity metrics: a syntactic, a semantic, and a linear combination of the two. We compare this tree selection methodology to a popular ensemble algorithm (majority voting) and to the baseline of randomly choosing one of the local models. In addition, we evaluate two alternative tree selection strategies: choosing the tree having the highest validation accuracy and reducing the original ensemble to five most representative trees. The comparative evaluation experiments are performed on six big datasets using two popular decision-tree algorithms (J48 and CART) and splitting each dataset horizontally into six different amounts of equal-size slices (from 32 to 1024). In most experiments, the syntactic similarity approach, named SySM—Syntactic Similarity Method, provides a significantly higher testing accuracy than the semantic and the combined ones. The mean accuracy of SySM over all datasets is {0.835} pm {0.065} for CART and {0.769} pm {0.066} for J48. On the other hand, we find no statistically significant difference between the testing accuracy of the trees selected by SySM and the trees having the highest validation accuracy. Comparing to ensemble algorithms, the representative models selected by the proposed methods provide a higher speed for big data classification along with being more compact and interpretable.

СИНТАКСИЧНИЙ ТИП (СХОЖІСТЬ ТА ДИФЕРЕНЦІАЦІЯ) В ІСПАНСЬКІЙ ТА УКРАЇНСЬКІЙ МОВАХ

The Indo-European syntactic system is very stable. This explains the fact that the Spanish and the Ukrainian, originating from a unique linguistic base, in their syntactic structure have a considerable number of common features. The syntactic similarity of comparative languages is manifested of the group of words, sentences and their elements. According to the classification criteria in the languages above mentioned there are a lot of syntactic similarities: are distinguished similar sentence members which are expressed by similar morphological modes. In both languages the order of the words in the sentence are very similar too. Both investigated languages are characterized by the common modes of expression of syntactic relationships and by similar links in word groups. The structure of simple and compound sentences also seems in both languages. Also there are simple and compound sentences, which are subdivided into coordinated and subordinated ones. On the other hand, each of the compared languages has its own distinctive features of the formal, semantic and functional deep level, which is explained by the specific national character of each of the studied languages. In the comparative languages, the group of words, as a succession of two or more significant words linked semantically and grammatically, represents the complex denomination of the phenomena of the reality. It is one of the very important syntactic units, with which we can build the sentences. In both languages there are a lot of isomorphic characters referring to groups of words. In relation to the degree of fusion of its components, the groups of words can be free or fixed. Thus in the groups of free words the lexical meanings of their constituents are conserved. They differ from the fixed ones whose components lose their lexical dependence. The fixed groups are divided into the dependents syntactically and phraseologically. In the comparative languages, the dependent groups syntactically perform a unique syntactic function. They do not split, one of its components is relaxed lexically. The complex character of groups of words can be achieved isomorphically in a similar way: by the amplification of the simple group with a dependent word, by the unification of the nucleus by a simple dependent group, by means of two grammatically dependent elements, by the extension of the direct object.

Hierarchical Transfer Learning Architecture for Low-Resource Neural Machine Translation

Neural Machine Translation(NMT) has achieved notable results in high-resource languages, but still works poorly on low-resource languages. As times goes on, It is widely recognized that transfer learning methods are effective for low-resource language problems. However, existing transfer learning methods are typically based on the parent-child architecture, which does not adequately take advantages of helpful languages. In this paper, inspired by human transitive inference and learning ability, we handle this issue by proposing a new hierarchical transfer learning architecture for low-resource languages. In the architecture, the NMT model is trained in the unrelated high-resource language pair, the similar intermediate language pair and the low-resource language pair in turn. Correspondingly, the parameters are transferred and fine-tuned layer by layer for initialization. In this way, our hierarchical transfer learning architecture simultaneously combines the data volume advantages of high-resource languages and the syntactic similarity advantages of cognate languages. Specially, we utilize Byte Pair Encoding(BPE) and character-level embedding for data pre-processing, which effectively solve the problem of out of vocabulary(OOV). Experimental results on Uygur-Chinese and Turkish-English translation demonstrate the superiorities of the proposed architecture over the NMT model with parent-child architecture.

Which Information Helps Resolve Recall Failures for Familiar People's Names?

Personal names are particularly susceptible to retrieval failures. In the present paper, studies describing people’s spontaneous strategies for resolving failures in recalling personal names as well as laboratory studies of experimentally induced resolution of name recall failures are reviewed. The review indicates that people frequently use spontaneous strategies based on a search for structural, semantic, and contextual information about the target person. On the other hand, both cueing and priming experimental studies have shown that providing phonological information may help resolve a recall failure, whereas providing structural or semantic information is usually not helpful. A possible explanation of this discrepancy between the spontaneous use of semantic/contextual information and the experimentally demonstrated uselessness of this kind of information is provided. Finally, the role of syntactical similarity (belonging or not to the same part of speech) in the efficiency of phonological priming is discussed.

"BRUSOV’S TRANSLATION OF THE POEM “DER NEUE COLUMBUS” BY FRIEDRICH NIETZSCHE"

"In the following the article the analysis of the translation of “Der neue Columbus” poem by Friedrich Nietzsche, done by V.Brusov is conducted. This article is to estimate the semantic, syntactic and grammatical similarities and differences between the original and the translated."

FlashProfile: a framework for synthesizing data profiles

We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strings. Real-world datasets, typically curated from multiple sources, often contain data in various syntactic formats. Thus, any data processing task is preceded by the critical step of data format identification. However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios. Prior techniques are restricted to a small set of pre-defined patterns (e.g. digits, letters, words etc.), and provide no control over granularity of profiles. We define syntactic profiling as a problem of clustering strings based on syntactic similarity, followed by identifying patterns that succinctly describe each cluster. We present a technique for synthesizing such profiles over a given language of patterns, that also allows for interactive refinement by requesting a desired number of clusters. Using a state-of-the-art inductive synthesis framework, PROSE, we have implemented our technique as FlashProfile. Across 153 tasks over 75 large real datasets, we observe a median profiling time of only ∼ 0.7s. Furthermore, we show that access to syntactic profiles may allow for more accurate synthesis of programs, i.e. using fewer examples, in programming-by-example (PBE) workflows such as Flash Fill.

The Present State of the Aspect Hypothesis: A Critical Perspective

Early research on the Aspect Hypothesis yielded a rigid developmental sequence for the acquisition of grammatical aspect, in which developing L2 learners applied morphemes to mark aspect in accordance with the inherent lexical aspect of verbal prototypes. While studies from a variety of L2 backgrounds have amassed evidence for this hypothesis, some recent research has questioned its generalizability (i.e. Comajoan, 2005; Izquierdo & Collins, 2008; Liskin-Gasparro, 2000; Lopez-Ortega, McManus, 2013; Salaberry, 2011). The present analysis of literature reviews the key tenets of the Aspect Hypothesis in order to examine how subsequent studies have demonstrated that L1 influence, syntactic similarities between L1 and L2, the role of inherent lexical aspect, and the use of pedagogical materials in classroom-based instruction may have implications for its accuracy in predicting learners’ use of temporal morphology. Such implications may affect the degree of accuracy with which this model predicts the acquisition of aspectual morphology in L2 learners, principally at early and advanced stages of proficiency. Lastly, this critical analysis identifies potential directions for future research that would strengthen or modify the recent claims that question the predictive accuracy of the Aspect Hypothesis.

Syntactic similarity of definable closures of Jonsson sets

Syntactic similarity of definable closures of Jonsson sets

Syntactic word embedding based on dependency syntax and polysemous analysis

Most word embedding models have the following problems: (1) In the models based on bag-of-words contexts, the structural relations of sentences are completely neglected; (2) Each word uses a single embedding, which makes the model indiscriminative for polysemous words; (3) Word embedding easily tends to contextual structure similarity of sentences. To solve these problems, we propose an easy-to-use representation algorithm of syntactic word embedding (SWE). The main procedures are: (1) A polysemous tagging algorithm is used for polysemous representation by the latent Dirichlet allocation (LDA) algorithm; (2) Symbols ‘+’ and ‘−’ are adopted to indicate the directions of the dependency syntax; (3) Stopwords and their dependencies are deleted; (4) Dependency skip is applied to connect indirect dependencies; (5) Dependency-based contexts are inputted to a word2vec model. Experimental results show that our model generates desirable word embedding in similarity evaluation tasks. Besides, semantic and syntactic features can be captured from dependency-based syntactic contexts, exhibiting less topical and more syntactic similarity. We conclude that SWE outperforms single embedding learning models.