Subject Predicate Research Articles

The fact that these are opinions: Processing and acceptability patterns of subjective vs. objective information embedded under<i> the fact that</i>

Language can convey both objective, fact-based information and subjective, opinion-based information. Previous research has focused on linguistic contexts that are associated with subjective information, such as information embedded under find. We aim to complement this existing work by exploring whether particular linguistic contexts are specifically associated with objective information. We report two psycholinguistic experiments testing the acceptability and processing of subjective and objective predicates embedded under the fact that. As a whole, the results suggest that subjective predicates embedded under the fact that are as acceptable and as easy-to-process as objective predicates, suggesting that this construction does not create a context that requires objective, fact-based information.

Generality, genericity and subjective predicates: What propositional attitude verbs, alien viruses, and COVID can tell us

In uttering a subjective opinion like Donuts are tasty, is a speaker expressing her own opinion or also making a generalization about people-in-general? While researchers largely agree that generic readings of subjective predicates exist, there is no consensus on how central genericity is for theories of subjective meaning. We report a psycholinguistic study that tests what influences the level of prevalence that comprehenders attribute to opinions, expressed with subjective predicates, about unfamiliar information. Specifically, if you overhear an alien expressing an opinion about an unfamiliar virus (e.g. The zorgavirus is dangerous), how many other aliens do you think share this alien's opinion? We find that the perceived generalizability of subjective predicates is modulated by the presence/absence of embedding under propositional attitude verbs (whether the speaker is explicitly mentioned with I think/consider) and by participants' extra-linguistic attitudes, namely their anxiety levels about COVID. This work uncovers a new link between subjective predicates and humans’ egocentric cognitive biases.

AI-based decision support system for public procurement

Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing evaluation models turn out to be essential to facilitate e-procurement procedures. With this contribution, we introduce our research to create a supporting system for the decision-making and monitoring process during the entire course of investments and contracts. This system employs artificial intelligence techniques based on natural language processing, focused on providing instruments for extracting useful information from both structured and unstructured (i.e., text) data. Therefore, we developed a framework based on a web app that provides integrated tools such as a semantic search engine, a summariser, an open information extraction engine in the form of triples (subject–predicate–object) for tender documents, and dashboards for analysing tender data.

Infer the missing facts of D3FEND using knowledge graph representation learning

PurposeMITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.Design/methodology/approachD3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.FindingsExperimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.Research limitations/implicationsDespite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.Practical implicationsLink prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.Originality/valueThe representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

중국어 2음절 신체 복합어의 품사 표기 문제

This paper analyzed the partiality of 42 two-syllable body compound words with different parts of speech as verbs and adjectives in various dictionaries through the modification of adverbs of degree. Type A body compound words consist of a subject predicate structure except for three predicate object structures. It is difficult to say that this type of compound word is lexicalized as a typical adjective because it is found that it receives little modification of adverbs of degree. Type B body compound words consist of predicate object structures, and it is a general view that as the internal closeness increases, they have been lexically converted from predicate object phrases into heterologous verbs. Many of these compound words can be seen as verb adjectives and concurrent verbs because they are relatively actively modified by adverbs of degree. The internal closeness of Chinese compound words is generally higher in the predicate object structure than the subject predicate structure. As the lexical progress of the predicate object structure with high internal closeness is more active, it is thought that type B compound words are more formulated by adverbs of degree.

Avoiding background knowledge: literature based discovery from important information

BackgroundAutomatic literature based discovery attempts to uncover new knowledge by connecting existing facts: information extracted from existing publications in the form of A rightarrow B and B rightarrow C relations can be simply connected to deduce A rightarrow C. However, using this approach, the quantity of proposed connections is often too vast to be useful. It can be reduced by using subjectrightarrow(predicate)rightarrowobject triples as the A rightarrow B relations, but too many proposed connections remain for manual verification.ResultsBased on the hypothesis that only a small number of subject–predicate–object triples extracted from a publication represent the paper’s novel contribution(s), we explore using BERT embeddings to identify these before literature based discovery is performed utilizing only these, important, triples. While the method exploits the availability of full texts of publications in the CORD-19 dataset—making use of the fact that a novel contribution is likely to be mentioned in both an abstract and the body of a paper—to build a training set, the resulting tool can be applied to papers with only abstracts available. Candidate hidden knowledge pairs generated from unfiltered triples and those built from important triples only are compared using a variety of timeslicing gold standards.ConclusionsThe quantity of proposed knowledge pairs is reduced by a factor of 10^3, and we show that when the gold standard is designed to avoid rewarding background knowledge, the precision obtained increases up to a factor of 10. We argue that the gold standard needs to be carefully considered, and release as yet undiscovered candidate knowledge pairs based on important triples alongside this work.

Im2Graph: A Weakly Supervised Approach for Generating Holistic Scene Graphs from Regional Dependencies

Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets ⟨subject—predicate—object⟩. Instead of building dedicated models for scene graph generation, our model tends to extract the latent relational information implicitly encoded in image captioning models. We explored dependency parsing to build grammatically sound parse trees from captions. We used detection algorithms for the region propositions to generate dense region-based concept graphs. These were optimally combined using the approximate sub-graph isomorphism to create holistic concept graphs for images. The major advantages of this approach are threefold. Firstly, the proposed graph generation module is completely rule-based and, hence, adheres to the principles of explainable artificial intelligence. Secondly, graph generation can be used as plug-and-play along with any region proposition and caption generation framework. Finally, our results showed that we could generate rich concept graphs without explicit graph-based supervision.

Exploring visual relationship for social media popularity prediction

Social media popularity prediction is an important channel to explore content sharing and communication on social networks. It aims to capture informative cues by analyzing multi-type data (such as images, user profiles, and text) to decide the popularity of a specified post. Intuitively, given an image, humans can volitionally focus on salient objects and relationships that are associated with their interests. For example, when we see the image including the relationship “elephant-attack-van”, it is more natural to increase our interest than the image with “elephant-near-van”. Therefore, exploiting such structural relationships is expected to help the prediction model search for evidence in support of the popularity of posts. However, most current works only focus on the global representation or the isolated objects, while ignoring the structure knowledge contained in images. To address this problem, we propose the relationship-aware social media popularity predictor. First, we extract inter-object relationships via a pre-trained scene graph generator. Then, we design a content-based filtering module to filter redundant relationships and capture the key 〈subject–predicate–object〉 information. Finally, we integrate relationship information with multi-type heterogeneous data and feed them into the CatBoost model for regression. Moreover, our predictor is capable of generating more intuitive interpretations by analyzing visual relationships in images to reasonably infer popularity scores. Extensive experiments conducted on the Social Media Prediction Dataset demonstrate that the proposed method can outperform other state-of-the-art models. Additional ablation studies and visualizations further validate the effectiveness and interpretability.

Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Abstract Purpose Given the information overload of scientific literature, there is an increasing need for computable biomedical knowledge buried in free text. This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements. Design/methodology/approach Taking cardiovascular research publications in China as a sample, we extracted subject–predicate–object triples (SPO triples) as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context. We introduced information entropy (IE) as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs (SO pairs) levels. Findings The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples. After evaluating the uncertainty of biomedical knowledge with IE, we identified the Top 10 SO pairs with highest IE, which implied the epistemic status pluralism. Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research. Research limitations The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words. The number of sentences surrounding a given triple may also influence the value of IE. Practical implications Our approach identified major uncertain knowledge areas such as diagnostic biomarkers, genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China. These areas are suggested to be prioritized; new hypotheses need to be verified, while disputes, conflicts, and contradictions need to be settled. Originality/value We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.

PEMEROLEHAN BAHASA INDONESIA ANAK USIA 5—6 TAHUN STUDI KASUS MUHAMMAD RAGIL SATRIA PUTRA AGUNG DALAM KAJIAN PSIKOLINGUISTIK

AbstractThis study aims to describe the acquisition of Indonesian children aged 5-6 years who are examined on the types of Indonesian sentences and patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung. The method used is descriptive method. The form of qualitative research. The data source is Muhammad Ragil Satria Putra Agung and the research data are all the utterances produced by Muhammad Ragil Satria Putra Agung in the form of sentences according to the selected problem formulation. The data collection technique used is the conversational engagement technique, recording technique and note-taking technique on the data card. The technique of testing the validity of the data is the persistence of observations and the adequacy of references. The results showed that: declarative sentences (news) as many as 272 sentences, imperative sentences (commands) as many as 32 sentences, interrogative sentences (questions) as many as 71 sentences. Based on the data classification, the patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung include: (SP) 246 patterns, (SPO) 8 patterns, (SP-Pel) 138 patterns, (SP-Ket) 29 patterns, (SPO- Note) 3 patterns, (SPO-Mop) 5 patterns, and (SP-Pel-Ket) 14 patterns. From the results of data analysis, the most dominant sentences obtained are declarative sentences, which are 272 sentences, while the sentence structure pattern that is most widely used is the Subject-Predicate (SP) pattern, which is 246 patterns. Keywords: Indonesian language acquisition, children aged 5-6 years, psycholinguistics.

DG‐based SPO tuple recognition using self‐attention M‐Bi‐LSTM

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject–predicate–object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

Consequences of evidential marking on the interpretation of subjective predicates: Experimental data from Korean

We report two psycholinguistic experiments on how two kinds of subjective predicates – predicates of personal taste (PPTs) and non-PPT multidimensional adjectives (MDs) – interact with evidential morphology in Korean. We build on theoretical discussion on PPTs and MDs, according to which the semantics of PPTs, but not MDs, makes reference to an individual’s first-person experience. We test whether and how the perceived generalizability of opinions containing PPTs or MDs is modulated by evidentiality. We tested three evidential morphemes in Korean: the direct evidential -te-, the hearsay (reportative) evidential -tay, and the inferential evidential -napo-, along with a baseline condition with no evidential marking. The results suggest that (a) with PPTs, type of evidential marking has no effect on the perceived generalizability of opinions, but (b) with MDs, use of the inferential evidential lowers perceived generalizability. We attribute these patterns to the first-person direct experience requirement of PPTs (which we suggest renders opinions with PPTs harder to generalize, regardless of evidential marking), and to the decreased reliability of inferential evidence. Our work provides novel evidence for a link between evidentiality, subjectivity and the perceived generalizability of opinions.

How to theorize about subjective language: a lesson from ‘de re’

Subjective language has attracted substantial attention in the recent literature in formal semantics and philosophy of language (see overviews in MacFarlane in Assessment sensitivity: relative truth and its applications, Oxford University Press, Oxford, 2014; van Wijnbergen-Huitink, in Meier, and van Wijnbergen-Huitink (eds) Subjective meaning: alternatives to relativism, De Gruyter, Berlin, pp 1–19, 2016; Lasersohn in Subjectivity and perspective in truth-theoretic semantics, Oxford University Press, Oxford, 2017; Vardomskaya in Sources of subjectivity, Ph.D. thesis, University of Chicago, IL, 2018; Zakkou in Faultless disagreement: a defense of contextualism in the realm of personal taste, Vittorio Klostermann, Frankfurt a. M., 2019b). Most current theories argue that Subjective Predicates (SPs), which express matters of opinion, semantically differ from ordinary predicates, which express matters of fact. We will call this view “SP exceptionalism”. This paper addresses SP exceptionalism by scrutinizing the behavior of SPs in attitude reports, which, as we will argue, significantly constrains the space of analytical options and rules out some of the existing theories. As first noticed by Stephenson (Linguist Philos 30(4):487–525, 2007a; Towards a theory of subjective meaning, Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, 2007b), the most prominent reading of embedded SPs is one where they talk about the attitude holder’s subjective judgment. As is remarked sometimes (Sæbø in Linguist Philos 32(4):327–352, 2009; Pearson in J Semant 30(1):103–154, 2013a), this reading is not the only one: embedded SPs may also talk about someone else’s, non-local, judgment. We concentrate specifically on such cases and show that non-local judgment is possible if and only if SPs are used within a DP that is outside main predicate position and that entire DP is read de re. We demonstrate that the behavior of SPs in attitude reports does not differ from that of ordinary predicates: it follows from general constraints on intersective modification and intensional quantification (Farkas in Szabolcsi (ed) Ways of scope taking, Springer, Dordrecht, pp 183–215, 1997; Musan in On the temporal interpretation of noun phrases, Garland, New York, 1997; Percus in Nat Lang Semant 8(3):173–229, 2000; Keshet in Good intensions: paving two roads to a theory of the de re/de dicto distinction, Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, 2008). We argue that this unexceptional behavior of SPs in fact has unexpected consequences for SP exceptionalism. Precisely because SPs have been argued to be semantically different from ordinary predicates, not all theories correctly predict these less-studied data: some overgenerate (e.g. Stephenson 2007a, b; Stojanovic in Linguist Philos 30(6):691–706, 2007; Sæbø 2009) and some undergenerate (e.g. McCready in McNally, and Puig-Waldmüller (ed) Proceedings of Sinn und Bedeutung, vol 11, pp 433–447, 2007; Pearson 2013a). Out of the currently available theories, only relativist accounts (Lasersohn in Linguist Philos 28(6):643–686, 2005; MacFarlane 2014; Bylinina in J Semant 34(2), 291–331, 2017; Coppock in Linguist Philos 41(2):125–164, 2018) predict the right interpretation, and only that interpretation. We thus present a novel empirical argument for relativism, and, more generally, formulate a constraint that has to be taken into consideration by any view that advocates SP exceptionalism.

Variasi Sapaan Ragam Akrab dan Ragam Santai Masyarakat Melayu Jambi dalam Komunikasi Verbal (Kajian Sosiolinguistik)

The purpose of this research is to describe the familiar and relaxed variety of speech greetings in the Malay community of Jambi. The focus of the research is related to 3 things, namely: 1) the forms of various forms of addressing familiar and casual types of discourse, 2) the factors behind the variations in addressing variations in various discourses, and 3) sentence patterns that contain variations in addressing familiar and casual types of discourse. in the verbal communication of the Jambi Malay community. This research is a qualitative research with a sociolinguistic approach. Field data collection uses ethnographic methods. Ethnographic techniques used include participatory observation, in-depth interviews, introspection, and domine analysis. Primary data comes from 8 informants and secondary data comes from documents. Data were analyzed based on socio-cultural and situational factors. Based on the results and discussion, it can be concluded that: 1) The forms of variation of greetings in the verbal communication of the Jambi Malay community are in the familiar and casual types. Forms of greeting used include pangilan kesanyangan, name abbreviations, intimate calls and special calls outside the language. This variation is found in verbal communication between family members whether they are older, the same age, or less. The variety of friendly and casual types of greetings outside of family relationships occurs in close friends or close friends, usually using special calls. 2) The factors that cause the use of familiar and casual types of greetings in verbal communication are determined by the factor of the proximity of the speaker and speaker relationship, the situation of speech and the topic of speech 4) The sentence pattern of the familiar variety tends to be Subject-Predicate (SP) while the casual variety Object (PO).

Dialectical Hegelian Logic and Physical Quantity and Quality

In Ontology, quality determines beings. The quality-quantity bipolarity reveals that a conceptual logical comprehension that can include negation must be a dialectical logic. Quality is a precise characteristic of something (or a subject predicate) capable of augmentation or diminution while remaining identical through differences or quantitative changes. Thus, quality and in opposition quantity are inextricably linked, giving definition to each other, so constituting a logical bipolarity. The theory is that a magnitude G is never separated from secondary qualities α and β, and therefore, a measure depends on a concrete quality Gα or Gβ, that is to say on one pole of a logical bi-pole. However, the particular number, the unit, that expresses the result of a measure is the quality G alone. Examples drawn from physical and chemical experiments illustrate these ideas and elaborate the structure of the concept of opposition between the secondary qualities α and β of a magnitude G.

Assertion, expression, experience

ABSTRACT It has been frequently observed in the literature that assertions of plain sentences containing predicates like fun and frightening give rise to an acquaintance inference: they imply that the speaker has first-hand knowledge of the item under consideration. The goal of this paper is to develop and defend a broadly expressivist explanation of this phenomenon: acquaintance inferences arise because plain sentences containing subjective predicates are designed to express distinguished kinds of attitudes that differ from beliefs in that they can only be acquired by undergoing certain experiences. Its guiding hypothesis is that natural language predicate expressions lexically specify what it takes for their use to be properly ‘grounded’ in a speaker's state of mind: what state of mind a speaker must be in for a predication to be in accordance with the norms governing assertion. The resulting framework accounts for a range of data surrounding the acquaintance inference as well as for striking parallels between the evidential requirements on subjective predicate uses and the kind of considerations that fuel motivational internalism about the language of morals. A discussion of how the story can be implemented compositionally and of how it compares with other proposals currently on the market is provided.

The role of grammar in transition-probabilities of subsequent words in English text.

Sentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf's law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject-predicate-object) locally re-order the word order in sentences that are sampled by the word generation process. We demonstrate that superimposing grammatical structure-as a local word re-ordering (permutation) process-on a sample-space reducing word generation process is sufficient to explain both, word frequencies and word-to-word transition probabilities. We compare the performance of the grammatically ordered SSR model in reproducing several test statistics of real texts with other text generation models, such as the Bernoulli model, the Simon model, and the random typewriting model.

Mining information from sentences through Semantic Web data and Information Extraction tasks

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

MELODI Presto: a fast and agile tool to explore semantic triples derived from biomedical literature.

ABSTRACTSummaryThe field of literature-based discovery is growing in step with the volume of literature being produced. From modern natural language processing algorithms to high quality entity tagging, the methods and their impact are developing rapidly. One annotation object that arises from these approaches, the subject–predicate–object triple, is proving to be very useful in representing knowledge. We have implemented efficient search methods and an application programming interface, to create fast and convenient functions to utilize triples extracted from the biomedical literature by SemMedDB. By refining these data, we have identified a set of triples that focus on the mechanistic aspects of the literature, and provide simple methods to explore both enriched triples from single queries, and overlapping triples across two query lists.Availability and Implementationhttps://melodi-presto.mrcieu.ac.uk/.Supplementary information Supplementary data are available at Bioinformatics online.

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.