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Troponymy is a type of sense inclusion specific to verbs. In the Igbo language, a west Benue-Congo sub family of the Proto Benue-Congo language family, troponymy includes sense ranges of the verb root -gbu 'kill' relating to 'manner verb-manner result' and 'manner verb-manner verb' relations. This study examines the nature of troponymy that involves the verb root -gbú across different semantic domains and verb classes. The aim is to ascertain whether the sense of negativity, 'kill' that is encoded in this verb is also visible in the compound constructions that involve the verb root. Compound constructions involving the verb root were extracted from a corpus using the AntConc software, and the data analyzed using the descriptive method of analysis. The findings of the study reveal that the manner and how an action is carried out are depicted in all the sense ranges when the verb -gbú forms a compound with another verb class, and the sense of negativity is retained, whether the verb that forms a compound with it encodes positivity or not. In addition, the troponym-hypernym relation of -gbú 'kill' is clearly delineated where -gbú as the hypernym is used to describe the different manners of killing somebody. However, when it occurs with other verb classes, what is observed is simply a troponymic relation in which the sense ranges denoting stativity and place are simply metaphorical extensions. [ABSTRACT FROM AUTHOR]

For sentence comprehension, information carried by semantic relations between constituents must be combined with other information to decode the constituent structure of a sentence, due to atypical and noisy situations of language use. Neural correlates of decoding sentence structure by semantic information have remained largely unexplored. In this functional MRI study, we examine the neural basis of semantic-driven syntactic parsing during sentence reading and compare it with that of other types of syntactic parsing driven by word order and case marking. Chinese transitive sentences of various structures were investigated, differing in word order, case making, and agent-patient semantic relations (i.e., same vs. different in animacy). For the non-canonical unmarked sentences without usable case marking, a semantic-driven effect triggered by agent-patient ambiguity was found in the left inferior frontal gyrus opercularis (IFGoper) and left inferior parietal lobule, with the activity not being modulated by naturalness factors of the sentences. The comparison between each type of non-canonical sentences with canonical sentences revealed that the non-canonicity effect engaged the left posterior frontal and temporal regions, in line with previous studies. No extra neural activity was found responsive to case marking within the non-canonical sentences. A word order effect across all types of sentences was also found in the left IFGoper, suggesting a common neural substrate between different types of parsing. The semantic-driven effect was also observed for the non-canonical marked sentences but not for the canonical sentences, suggesting that semantic information is used in decoding sentence structure in addition to case marking. The current findings illustrate the neural correlates of syntactic parsing with semantics, and provide neural evidence of how semantics facilitates syntax together with other information.

IntroductionNature is a theophany of divinity and motion which human lives are considered on it. Human essence along with nature, could lead to a spiritual journey and also help manifest new science to a better green and clean world. Theosophist person is watchful in holly nature and in his search toward the semantics of all creation, could feel the essence of all the beings. Mollasadra is one of those theosophists and philosophers, who have a knowledge of the being of things, and his spiritual journey is based on the essential idea of existence. That’s why his philosophy could be applicable in all periods of human science. His model of Transcendent theosophy which is based on Human, reality, and existence, is an important subject that can be applicable to the model of Human, Nature, and Architecture.The aim of this research is to express the semantic relation of divinity within nature, with human transcendence of soul, in order to reach new philosophical and scientific relation between Mollasadra's theories of nature with Biophilic technologies, to increase the quality of life. Literature ReviewTrans-substantial Motion theory of Molasadra explains that the being of all existents, from imperfection to perfection are in motion. Therefore, the essence of all existents is in motion. The proof of such theories in the material and scientific world is important. Digital technologies in all fields are a good example of creating a virtual connection between things and their beings.According to philosophical theories of Fundamental reality of existence, Gradation of being and Trans- Substantial Motion, Mollasadra proves that nature with all its little bits is a motion, and the least concept for it is to attain perfection. Nature is the whole material in front of the immaterial world, while nature from another point of view is an inner power that orders the whole creation with divine order. It also means the simple indivisible essence of humans. Therefore nature via being a material output is also an inner quality that is one essence of humans and the world. The qualitative side of nature is applicable to basic philosophical theories.The world today is dealing with different sorts of problems in the living environment. Sustainable development was a perfect solution to this damaged world, sustainable design attempts to recognize this chaos and bring the world peace and welfare. A unity of reaching and feeling happiness is percept within all concepts and theories of trans-substantial motion to sustainable design and smart biophilic technologies via energy transformation. These theories and methods were all attempting to create a better living environment for human beings.MethodologyThe research is an interdisciplinary inquiry based on Mollasadra's philosophy and Biophilic design. Therefore understanding the clear vision of professors on these two topics was essential. Also since the aim of this research is the relation between the spiritualities of nature and the perfection of the human soul via creating scientific and philosophical aspects, designing space according to new modern biophilic technologies, in order to improve the quality of life in the living spaces, is important.The qualitative research method (Grounded theory) of Strauss and Corbin, which tries to reveal the correlation of interaction of beings, is used in this research. Therefore eighteen deep interviews, in the field of Philosophy and Architecture have been done, and the data have been analyzed with MAXQDA software. As a result, 89 definitions were found which were categorized in 24 meanings, and landed on a paradigm.ResultsThe semantic analysis of the relation between Humans and Nature according to Mollasadra's Transcendental Knowledge, speaks about Biophilic Design and Mollasadra philosophy. The interdisciplinary research on these two subjects is an innovation that expresses a philosophical matter in the modern world of design.The findings of the definitions, purpose, and categorized meanings, were assorted in two tables. Table 1, defines nature as the reason for human development, which proves that, through all periods of human development, nature played a significant role in the perfection of mind, body, and living environment. Table 2 is about Divine Vicegerent. This is a term that arises from the Quran, but has a universal meaning in all civilizations. This part of the research emphasizes the relation of Divine Vicegerent and Architect, on how according to spiritualities, they create and build their living environment, which via perfecting the soul will improve quality of life.ConclusionMollasadra’s Transcendental Knowledge, along with many supernaturalism and theism philosophies, attempts to reach wisdom that physically and spiritually improves human well-being and acts in order to create better living conditions. This attempt is based on the relation between the essence of nature with human beings. Trans-substantial motion through reaching the reality of essence transforms the human soul to feel happiness and let other human beings enjoy it, and living in an intelligent architectural space, that its aim is beneficiary renewable energies in the buildings, accomplishes this concept. Living in Biophilic places raises both the human soul and the quality of life. The industrial world today, is in need of new technologies to create natural spaces in small human living environments such as House Vertical Farm or Green Living Walls. The findings of the paradigm express the clear philosophical relation of nature and the human living environment, which explains the essence of human beings in the vicinity of nature, flows to its perfection.

Functions of Pairs of Words from Sets of Non-Binary Semantic Oppositions in Sentences from the Texts of the National Corpus of Polish The article attempts to investigate whether pairs of words from sets of non-binary semantic oppositions perform the same functions in sentences as strong semantic binary oppositions. Six noun pairs were selected for analysis: summer/winter, arm/leg, cat/dog, coffee/tea, trousers/skirt and telephone/letter. A total of 1,200 sentences in which members of one of these six pairs co-occur were analysed; they were selected from the balanced sub-corpus of the National Corpus of Polish. A set of nine basic functions is presented, which has been applied in works on various languages in recent decades. The functions are identified by determining the mutual relationship between the members of the pair, based on the semantic-syntactic analysis of the immediate context and the meaning of the whole sentence. The present study confirms the usefulness of almost all the functions from the set for describing the way the analysed pairs are used in the sentences from the corpus. Apart from this, it was found that the same two functions are the strongest in the present study as in this type of analysis concerning strong binary oppositions. Funkcje par wyrazów ze zbiorów opozycji semantycznych wieloczłonowych w zdaniach z tekstów Narodowego Korpusu Języka Polskiego W artykule podjęto próbę sprawdzenia, czy pary wyrazów ze zbiorów opozycji semantycznych wieloczłonowych pełnią w zdaniach takie same funkcje, jak pary stanowiące silne opozycje semantyczne dwuczłonowe. Do analizy wybrano sześć par rzeczownikowych: lato/zima, ręka/noga, kot/pies, kawa/herbata, spodnie/spódnica i telefon/list. Ze zrównoważonego podkorpusu Narodowego Korpusu Języka Polskiego wyselekcjonowano 1200 zdań, w których współwystępują człony jednej z tych sześciu par. Przedstawiono zestaw dziewięciu podstawowych funkcji, wykorzystywany w ostatnich dziesięcioleciach w pracach dotyczących różnych języków. Funkcje wyodrębniane są poprzez określenie wzajemnej relacji między członami pary na podstawie analizy semantyczno-składniowej kontekstu bezpośredniego i ustalenia znaczenia całego zdania. Niniejsze badanie potwierdziło użyteczność prawie wszystkich funkcji z zestawu do opisu sposobu użycia badanych par w zdaniach z korpusu. Ponadto stwierdzono, że w obecnym badaniu najsilniejsze są te same dwie funkcje co w analizach tego typu dotyczących silnych opozycji binarnych.

The problem of Semantic Textual Similarity (STS) is a significant issue in Natural Language Processing (NLP). STS recognizes and measures semantic relations between two texts. Since the ability to determine the degree of the semantic relationship between sentence pairs is an integral part of machines that understand and infer natural language, we intend to improve the performance of the neural network systems computing the degree of the semantic relation. We propose a graph-U-Net model that operates on a dependency graph and is placed on top of a transformer. Our proposed model indicates the importance of the words in the sentence by assigning the words to several levels while a score as a degree of importance is computed for each level. These scores are used as a weighted average to produce the final result. The importance of the words is new information that our proposed model extracts from the STS and Paraphrase Identification (PI) datasets. We examine the effect of the proposed model on the performance of some transformers in computing semantic relation scores. We use STS2017 and MRPC datasets to evaluate our proposed model. Experimental evaluations show that compared to the transformers, our proposed model obtains a higher value of Pearson and Spearman correlation coefficients and also generates valuable representations for each input so that they improve the Pearson and Spearman values of the systems computing the degree of semantic equivalence between two texts. [ABSTRACT FROM AUTHOR]

To understand the audience's subjective perception of quality of service (QoS), it is important to analyze the data acquired from the logistics service logs and online evaluation system reasonably and effectively. Based on the analysis, rational improvement measures and decision suggestions can be developed to enhance the QoS. However, modern logistics service departments often face various business needs and service objects at the same time. If the evaluation subjects and their relationships are unclear in the service evaluation data, the sentiment analysis result of the text is a coarse-grained evaluation of the service as a whole. The lack of fine-grained pertinent evaluation results will hinder the improvement of specific management measures. To solve the problem, this paper designs an attention-based long short-term memory network (AT-LSTM) to divide the service reviews into ten topic relations, and then builds a deeper attention LSTM with aspect embedding (AE-DATT-LSTM). The weight-sharing bidirectional LSTM (BiLSTM) trains the topic word vectors and the text word vectors, and fuses the resulting topic features and text features. After the processing of the deep attention mechanism, the sentiment class of each evaluation topic is obtained by the classifier. Finally, several experiments were carried out on different public datasets. The results show that our approach surpasses the previous attention-based sentiment analysis models in accuracy and stability of service quality sentiment analysis. The introduction of topic features and deep attention mechanism is of great significance for the QoS-based sentiment classification b, and provides a feasible method for other fields like public opinion analysis, question answering system, and text reasoning.

In-Text Mining, Information Retrieval (IR), and Natural Language Processing (NLP) dig out the important text or word from an unstructured document is coined by the technique called Keyword extraction. It helps to identify the core information about the document in specific. Instead of going through the entire document, this method helps to retrieve sufficient information instantly in a short span of time. It is essential to mine the meaningful word from the document in text analytics. The proposed system has been based on semantic relation to extracts the keyword from unstructured text documents by means of practice like Latent Semantic Analysis (LSA). In view of this method, there exists a semantic relation between the sentences available in the document and the words. Extracted text permits to signify text in a strong way and has a high preference to carry more important information about the sentences. In this regard, LSA has produced better outcomes when compared with the TF-IDF, RAKE, YAKE, and Text Rank algorithm. Consequently, the keyword extraction has been occupied in Automatic Question Generation (ACQ) to generate the Fill up the blank (FB) and Multiple Choice Questions (MCQ) with distractor set. The top five, ten keywords are involved in questionable generation. The proposed system could be implemented in the question generation system to assess the skill level of the learner.

Previous research has revealed two different old/new effects, the early mid-frontal old/new effect (a.k.a., FN400) and the late parietal old/new effect (a.k.a., LPC), which relate to familiarity and recollection processes, respectively. Although associative recognition is thought to be more based on recollection, recent studies have confirmed that familiarity can make a great contribution when the items of a pair are unitized. However, it remains unclear whether the old/new effects are sensitive to the nature of different semantic relations. The current ERP (event-related potentials) study aimed to address this, where picture pairs of thematic, taxonomic, and unrelated relations served as stimuli and participants were required to discriminate the pair type: intact, rearranged, "old + new", or new. We confirmed both FN400 and LPC. Our findings, by comparing the occurrence and the amplitudes of these two components, implicate that the neural activity of associative recognition is sensitive to the semantic relation of stimuli and depends more on stimulus properties, that the familiarity of a single item can impact the neural activities in discriminating associative pairs, and that the interval length between encoding and test modulates the familiarity of unrelated pairs. In addition, the dissociation between FN400 and LPC reinforces the dual-process models. [ABSTRACT FROM AUTHOR]

The features of modeling a graphic-verbal polycode text, including a static image and an accompanying inscription, are considered. The study was conducted on the example of a demotivator dedicated to the problems of mass self-isolation at the very beginning of the pandemic and the introduction of restrictive measures. Significant semantic components, represented as part of only the iconic component, only the verbal component, and also as part of the verbal and iconic components at the same time are established. The semantic relations between the selected semantic components are revealed, the types of these links, revealing the different nature of their correlation are determined. On the basis of the data obtained, a network model of the considered static polycode text in the form of a semantic network was built. Cases of semantic components correlation are considered, reflecting the generally objective aspects of the situation and unrealistic ideas based on irony and hyperbole to create a comic effect. Based on quantitative analysis, representative semantic relations were established: “partitive”, “localization (in)”, “attributive”, “subject-object”. Non-representative semantic relations between the semantic components in the analyzed polycode text are revealed: “coincidence”, “localization (on)”, “temporal”, “subject-instrument”, “subject-result”.

Combinatorial relational reasoning in neural networks used for object detection is usually static; therefore, it cannot selectively fuse visual information and semantic relations, which limits their performance. To address this problem, we propose a relational graph routing network (RGRN) that enables the dynamic interaction of visual and semantic features. The network consists of a dynamic graph network, dual path-sharing module, and relational routing interaction module. First, we used a data-driven technique to obtain the semantic information between tags from the dataset. Rich semantic information was obtained by calculating the similarity between tags. Second, the two types of semantic information were fused using a dynamic graph network to capture high-level semantic information. The visual and semantic features are then filtered and encoded through the dual path-sharing module to obtain enhanced visual and semantic features. Finally, three units were used to dynamically fuse visual and semantic information in the relational routing interaction module, which densely links the three units and routers to construct a routing space that can autonomously decide on the optimal fusion path through model learning. A series of experiments was conducted on the MS COCO dataset. RGRN achieved 54.7% box AP on object detection, which was 2.8% box AP higher than that of the Cascade Mask R-CNN. The experimental results show that the routing space enables better interaction between visual and semantic information. Therefore, our method can achieve better performance than many state-of-the-art methods. • We use similarity to gain rich semantic information from the datasets' labels. • To collect high-level semantic data, we propose a dynamic graph network. • We create a dual path-sharing module that records crucial visual and semantic data. • We propose a relational routing interaction module that constructs a routing space. • The method can choose the best fusion path automatically in the routing space. [ABSTRACT FROM AUTHOR]

Aspect sentiment triplet extraction is the most recent subtask of aspect-based sentiment analysis, which aims to extract triplets information from a review sentence, including an aspect term, corresponding sentiment polarity, and associated opinion expression. Although existing researchers adopt an end-to-end method to avoid the error propagation caused by the pipeline manner, they cannot effectively establish the semantic association between aspects and opinions when extracting triples. Furthermore, utilizing sequence tagging methods in extraction and classification tasks will lead to problems, such as increased model search space and sentiment inconsistency of multi-word entities. To tackle the above issues, we propose an enhancing aspect and opinion terms semantic relation framework to make extract triplets more exact by fully capturing interactive information. Specifically, dual convolutional neural networks are used to construct aspect-oriented and opinion-oriented features respectively, the semantic relation is considered through the attention mechanism, and then feedback to each extraction task. We also employ a span-based tagging scheme to extract multiple entities directly under the supervision of span boundary detection accurately predict sentiment polarity based on span distance. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate that our model significantly outperforms all baseline methods. [ABSTRACT FROM AUTHOR]

Copyright of Slavistična Revija is the property of Slavisticno Drustvo Slovenije and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image-text matching, which aims at precisely measuring the visual-semantic similarities between images and texts, is a fundamental research topic in multimedia analysis domain. Current methods have obtained an impressive performance by taking advantage of Transformer architecture. However, most of them only consider inter-modal relationships to mine the image-text semantic correspondences, which makes them hard to accurately measure the similarity when facing similar images and text due to the cross-modal semantic interferences. In this work, to tackle the issue mentioned above, we propose a Cross-Modal Semantic Interference Suppression (CMSIS) method, which incorporates intra-modal fine-grained semantics and unmatched segments to suppress the semantic influences caused by similar heterogeneous data points. The intra-modal fine-grained semantics are utilized to push similar images or text away in the learned latent embedding space for better matching results. To further suppress the cross-modal semantic interferences among similar data points, the unmatched segments that can provide explicit clues to distinguish similar images or text, is also adopted. Experimental results on two popular multimodal datasets have demonstrated that the proposed CMSIS outperforms a range of baselines. [ABSTRACT FROM AUTHOR]

Previous research has revealed two different old/new effects, the early mid-frontal old/new effect (a.k.a., FN400) and the late parietal old/new effect (a.k.a., LPC), which relate to familiarity and recollection processes, respectively. Although associative recognition is thought to be more based on recollection, recent studies have confirmed that familiarity can make a great contribution when the items of a pair are unitized. However, it remains unclear whether the old/new effects are sensitive to the nature of different semantic relations. The current ERP (event-related potentials) study aimed to address this, where picture pairs of thematic, taxonomic, and unrelated relations served as stimuli and participants were required to discriminate the pair type: intact, rearranged, “old + new”, or new. We confirmed both FN400 and LPC. Our findings, by comparing the occurrence and the amplitudes of these two components, implicate that the neural activity of associative recognition is sensitive to the semantic relation of stimuli and depends more on stimulus properties, that the familiarity of a single item can impact the neural activities in discriminating associative pairs, and that the interval length between encoding and test modulates the familiarity of unrelated pairs. In addition, the dissociation between FN400 and LPC reinforces the dual-process models.

The goal of this research is to design and implement a chatbot for querying Wordnet semantic relations. The study creates a contextual chatbot named WordnetBot, a web application that utilizes the use of technologies such as Dialogflow, React, NodeJS, Javascript, and MariaDB. The Wordnet database which leverages all other dictionaries due to its semantic relations representation was used as the data source. Phrase Structure Analysis extracts the keyword and the semantic relation from a user’s message or query. It complements the Machine Learning and AI capabilities of Dialogflow in the analysis. The researcher designed an architectural framework for the integration of the different components of WordnetBot.

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Attribute learning has improved the performance in scene understanding and scene recognition. However, there are many attributes described by words or short texts in a static scene and traffic crowd scene. If there are two similar scenes, the semantic relationship topology structures of corresponding attribute groups of the two scenes are also homogeneity. But it is difficult to learn a semantic relation topology projection across semantic text data and visual data. To solve the problem, we construct approximate homeomorphism mapping based on the scene attributes semantic relational regularization. Hence, we propose a novel attribute semantic topological relationship regularization based scene attribute semantic learning(ARSL) method for scene semantic understanding. We establish a transport and travel scene recognition model based on attribute semantic features which are achieved by the proposed ARSL algorithm. In order to verify the proposed method, the experiments are implemented on the static transport-travel scene dataset and dynamic transport-travel crowds scene dataset respectively. The static transport-travel scene dataset is constructed by the SUN Attribute dataset including images and texts. However, the dynamic transport-travel crowds scene dataset is constructed through the WWW Crowd dataset including videos and texts, and the dynamic transport-travel crowd scene dataset is named as the WWW Crowd-Sub dataset. The performances of the proposed method are improved by 38.48% and 17.51% on the SUN Attribute dataset and WWW Crowd-Sub dataset respectively. The experimental results on the SUN Attribute dataset and WWW Crowd-Sub dataset demonstrate that the proposed approach has superior performance compared to state of the art. It can be demonstrated that the performance of the proposed ARSL method is effective against static transport-travel scene and dynamic transport-travel crowd scene.

Automatic image captioning has achieved a great progress. However, the existing captioning frameworks basically enumerate the objects in the image. The generated captions lack the real-world knowledge about named entities and their relations, such as the relations among famous persons, organizations and buildings. On the contrary, humans interpret images in a specific way by providing real-world knowledge with relations of the aforementioned named entities. To generate human-like captions, we focus on captioning the images of news, which could provide real-world knowledge of the whole story behind the images. Then we propose a novel model that makes captions closer to the human-like description of the image, by leveraging the semantic relevance of the named entities. The named entities are not only extracted from news under the guidance of the image content, but also extended with external knowledge based on the semantic relations. In detail, we propose a sentence correlation analysis algorithm to selectively draw the contextual information from news, and use entity-linking algorithm based on knowledge graph to discover the relations of entities with a global sight. The results of extensive experiments based on real-world dataset which is collected from the news show that our model generates image captions closer to the corresponding real-world captions.

Image-Text Matching is one major task in cross-modal information processing. The main challenge is to learn the unified visual and textual representations. Previous methods that perform well on this task primarily focus on not only the alignment between region features in images and the corresponding words in sentences, but also the alignment between relations of regions and relational words. However, the lack of joint learning of regional features and global features will cause the regional features to lose contact with the global context, leading to the mismatch with those non-object words which have global meanings in some sentences. In this work, in order to alleviate this issue, it is necessary to enhance the relations between regions and the relations between regional and global concepts to obtain a more accurate visual representation so as to be better correlated to the corresponding text. Thus, a novel multi-level semantic relations enhancement approach named Dual Semantic Relations Attention Network(DSRAN) is proposed which mainly consists of two modules, separate semantic relations module and the joint semantic relations module. DSRAN performs graph attention in both modules respectively for region-level relations enhancement and regional-global relations enhancement at the same time. With these two modules, different hierarchies of semantic relations are learned simultaneously, thus promoting the image-text matching process by providing more information for the final visual representation. Quantitative experimental results have been performed on MS-COCO and Flickr30K and our method outperforms previous approaches by a large margin due to the effectiveness of the dual semantic relations learning scheme. [ABSTRACT FROM AUTHOR]

• Word order, case markers, and semantics are combined to decode a sentence structure. • Semantic-driven parsing in unmarked non-canonical sentences activates Broca's area. • Different types of syntactic parsing recruit a common neural substrate. For sentence comprehension, information carried by semantic relations between constituents must be combined with other information to decode the constituent structure of a sentence, due to atypical and noisy situations of language use. Neural correlates of decoding sentence structure by semantic information have remained largely unexplored. In this functional MRI study, we examine the neural basis of semantic-driven syntactic parsing during sentence reading and compare it with that of other types of syntactic parsing driven by word order and case marking. Chinese transitive sentences of various structures were investigated, differing in word order, case making, and agent-patient semantic relations (i.e., same vs. different in animacy). For the non-canonical unmarked sentences without usable case marking, a semantic-driven effect triggered by agent-patient ambiguity was found in the left inferior frontal gyrus opercularis (IFGoper) and left inferior parietal lobule, with the activity not being modulated by naturalness factors of the sentences. The comparison between each type of non-canonical sentences with canonical sentences revealed that the non-canonicity effect engaged the left posterior frontal and temporal regions, in line with previous studies. No extra neural activity was found responsive to case marking within the non-canonical sentences. A word order effect across all types of sentences was also found in the left IFGoper, suggesting a common neural substrate between different types of parsing. The semantic-driven effect was also observed for the non-canonical marked sentences but not for the canonical sentences, suggesting that semantic information is used in decoding sentence structure in addition to case marking. The current findings illustrate the neural correlates of syntactic parsing with semantics, and provide neural evidence of how semantics facilitates syntax together with other information. [ABSTRACT FROM AUTHOR]

Semantic relations between words like synsets are used in automatic ontology production which is a strong tool in many NLP tasks. Synset extraction is usually dependent on other languages and resources using techniques such as mapping or translation. In our proposed method, synsets are extracted merely from text and corpora. This frees us from the need for special resources including Word-Nets or dictionaries. The representation model for words of corpus is based on Vector Space model and the most similar words to each are extracted based on common features count (CFC) using a modified cosine similarity measure. Furthermore, a graph-based soft clustering approach is applied to create clusters of synonymous words. To examine performance of the proposed method, Extracted synsets were compared to other Persian semantic resources. Results show an accuracy of 80.25%, which indicates improvement in comparison to the 69.5% accuracy of pure clustering by committee method.

WordNet is a large lexical database of English language, in which, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets). Each synset expresses a distinct concept. Synsets are interlinked by both semantic and lexical relations. WordNet is essentially used for word sense disambiguation, information retrieval, and text translation. In this paper, we propose several automatic methods to extract Information and Communication Technology (ICT)-related data from Princeton WordNet. We, then, add these extracted data to our Persian WordNet. The advantage of automated methods is reducing the interference of human factors and accelerating the development of our bilingual ICT WordNet. In our first proposed method, based on a small subset of ICT words, we use the definition of each synset to decide whether that synset is ICT. The second mechanism is to extract synsets which are in a semantic relation with ICT synsets. We also use two similarity criteria, namely LCS and S3M, to measure the similarity between a synset definition in WordNet and definition of any word in Microsoft dictionary. Our last method is to verify the coordinate of ICT synsets. Results show that our proposed mechanisms are able to extract ICT data from Princeton WordNet at a good level of accuracy.