Robust natural language analysis

Previous findings from event-related brain potentials (ERPs) indicate that adults who stutter (AWS) exhibit processing differences for visually presented linguistic information. This study explores how neural activations for AWS may differ for a linguistic task that does not require preparation for overt articulation or engage the articulatory loop for silent speech. Syntactic and semantic processing constraints were examined in AWS and adults who are normally fluent (AWNF) by assessment of their behavioral performance and ERPs in a natural speech listening task. AWS performed similarly to AWNF in identifying verb-agreement violations and semantic anomalies, but ERPs elicited by syntactic and semantic constraints indicated atypical neural functions for AWS. ERPs of the AWNF displayed an expected N400 for reduced semantic expectations and a typical P600 for verb-agreement violations. In contrast, both N400s and P600s for the semantic and verb-agreement conditions were observed in the ERPs of the AWS. The findings suggest that AWS may engage semantic-syntactic mechanisms more generally for semantic and syntactic processing. These findings converge with earlier studies using visual stimuli to indicate that whereas linguistic abilities are normal in AWS, underlying brain activity mediating some aspects of language processing may function differently.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Syntactic and semantic structure in the recall of orders of approximation to English

Korean language has different structural properties which are controlled by semantic constraints of verbs. Also, most of Korean sentences are complex sentences which consisted of main clause and embedded clause. Therefore it is difficult to describe appropriate syntactic grammar or constraint for the Korean language and the Korean parsing causes various syntactic ambiguities. In this paper, we suggest how to describe CFG-based grammar using sentence patterns as syntactic constraint and solve syntactic ambiguities. To solve this, we classified 44 sentence patterns including complex sentences which have subordinate clause in Korean sentences and used it to reduce syntactic ambiguity. However, it is difficult to solve every syntactic ambiguity using the information of sentence patterns. So, we used semantic markers with semantic constraint. Semantic markers can be used to solve ambiguity by auxiliary particle or comitative case particle.

For practical research in natural language processing, it is indispensabh . to develop a large scale sentantic dictionary for computers. It is especially important to improve the tecbuiques ibr compiling semantic dictionaries torn natural language texts such as those in existing human dictionaries or in large corpora. 11owever } there are at least two ditIicultics in analyzing existing texts: tbe problem of syntactlc ambiguities and the problem of polysemy. Our approach to solw, these difficulties is to make use of translation exam- pies in two dis[inet languagcs that haw (luitc different sylltactic structures and word mealtings. The reason we took tbis approach is that in many cmcs both syn tactic alrd scnlantic ambiguities arc resolved by comparing analyzed results from botb lauguages. In this paper, we propose a method ibr resolving the syntac tie ambiguities of translation examples of bihngual corpora and a method for acquiring lexical knowledge, sucb a.s case frames of verbs and attribute sets of noutts.

Tableau-Based Revision for Expressive Description Logics with Individuals

In this paper, we show how a constraint-based approach influences the modeling of (1) preposition lexicalization and of (2) the choice of syntactic structure in natural language generation (in particuler in French). We concentrate on the linguistic description, which is the most challenging. The CSP procedures themselves are then rather straightforward. In the first case, preposition choice depends on the verb and its requirements, on the one hand, and the characteristics of the NP the preposition heads, on the other hand. In the second case, the choice of a particular syntactic structure depends on syntactic, semantic and pragmatic constraints on a verb, its arguments and on the parameters of the semantic representation. These dependencies may induce somewhat contradictory expectations. A constraint-based approach introduces a declarative model of these complex relations, allowing to identify all the possible (1) lexical choices or (2) syntactic structures which can be predicted from lexical descriptions.KeywordsSyntactic StructureConstraint Satisfaction ProblemNatural Language GenerationLinguistic DescriptionMovement VerbThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural language texts. That technique relied on the parser recognizing specific syntactic constructions, which had limited coverage. This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse. We outline how narrative function combined with query-driven abduction enables the same kinds of information to be extracted from natural language texts. Moreover, we also show how the same technique can be used to extract type-level qualitative representations from text, and used to improve performance in playing a strategy game.

It's a world of full of information. Data is one of the important element of this era. One of the major sources of data is social media platforms like Twitter, Facebook etc. Everyday social media generates lot of data. It's a free form of communication where people communicate with each other without any restriction. Users can post anything on social media. However, human tendency of speaking about non-existing or source-less thing makes it unclear or create unuseful information and becomes unreliable information. This casual and word-of mouth form of communication leads to generate uncertain data and quality of information from factuality point of view becomes primary concern in social media. So, uncertainty detection is important in social media. Uncertainty detection in natural language text becomes challenging because dealing with natural language text is pretty complicated thing. Uncertainty is an important field of linguistics. Basically, it means “lack of information”. So, a statement whose truth value cannot be determined is considered as uncertain. Linguistics and Natural Language Processing aims at classifying factual and uncertain proposition. In this chapter, comparisons of classification algorithm have been experimented to detect uncertain propositions in Twitter data of food price crisis. Comparative analysis of classification algorithm Naive Bayes and Support Vector Machine approach is done to detect uncertain propositions of tweets related to food price crisis. A model is trained to classify certain or uncertain proposition using a training file which is annotated using cue words available in English language text. Output of algorithm is the class showing given proposition is certain or uncertain. The objective of this chapter is to have a comparative analysis of text classification approach to detect uncertain events of Twitter data of food price crisis and to improve the accuracy of uncertainty classification approaches in order to detect uncertain events in natural language processing.

This extended abstract focuses on the crucial task of accessing meaning directly in natural language texts (NLT) and databases for the purposes of information assurance and security (IAS) and, especially, information intelligence. It is pretty obvious to all those involved in these areas that much if not most of the information that is pertinent for the various tasks in these fields is represented by NLTs and data from the Web, intercepted documents, research and position papers, print and media reports, etc. The paper deals with a unique approach, ontological semantics (OntoSem), that makes it possible to access the comprehensive meaning of texts and databases directly, emulating and approximating human understanding, and to manipulate it in various real-life applications, rather than trying to circumvent semantics with the juxtaposition of practically unimplementable formalisms, increasingly detailed syntactic parsers or ingenious cluster statistics on NLTs, which is most of the field of natural language processing (NLP) has been playing with in the last two decades.

Today, natural language text processing automation is widely used in various fields (public sector, science, education, media, everyday services, etc.). This requires appropriate software tools (services) that can provide such automated processing. The purpose of this article is to analyse and study the problems of modelling the processes of linguistic analysis of Ukrainian natural language texts and designing appropriate software with the definition of the functional capabilities of its components. The research methodology encompasses a comparative analysis of the primary software solutions in this subject area (linguistic analysis of texts presented in natural language), systematisation of approaches to automated text processing, and formalisation of these processes in the form of a corresponding process model. The scientific novelty of the research lies in the analysis of current problems in systems supporting the processes of automated processing of texts in natural language, in particular Ukrainian; the development of a process model that combines different stages and phases of linguistic analysis of texts; and the design of an appropriate software solution to support this model. Conclusions. The work examines the main problems of natural language text processing; identifies the main methods of processing Ukrainian-language texts; analyses and systematises modern systems that support individual stages of automated natural language text processing; a project for an author’s software solution that will provide linguistic analysis of Ukrainian-language texts is described; a process model for linguistic analysis of natural language texts is proposed. The use of the proposed process model by users and developers will facilitate the deployment of high-quality Ukrainian-language services; on the part of institutions (relevant software products) during the modification of the linguistic analysis system, it will contribute to obtaining a more objective view of the most common problems of linguistic analysis (so-called language problems), lexicon gaps, and priorities for updates to Ukrainian-language NLP.

We present a system for extraction of temporal expressions from French texts. The identification of the temporal expressions is based on a context-scanning strategy (CSS) which is carried out by two complementary techniques: search for regular expressions and left-to-right and right-to-left local chart-parsing. A number of semantic and distant-dependency constraints have been integrated to the chart-parsing procedure in order to improve the precision of the system.

In current days emotion analysis has been attracting many users to continue their research towards detecting various emotions from natural text. Almost a lot of study is being done in the area of Artificial Intelligence (AI), which mainly focuses on identifying the human state rather than digging the inner reason behind those state or emotions, like why those states are not recognized properly. There were many failures in the primitive methods for emotion detection because of improper correlation among emotions. Hence this motivated me to design a new model, which can fill the gap between emotion recognition and emotion correlation mining through natural language text from text conversations. In this proposed model, we try to mine emotion correlation from emotion recognition through natural text by using various kinds of features and by applying machine learning and deep learning pre-trained models. The main features are anger, disgust, fear, guilt, joy, sadness, and shame. By conducting various experiments on proposed models by taking a sample dataset collected from the Kaggle website, we try to observe the performance metrics of each and every individual algorithm in order to detect the emotions very accurately from natural language text.

Ontologies are used to represent knowledge and they have become very important in the Semantic Web era. Ontologies evolve continuously during their life cycle to adapt to new requirements and needs, especially in the biomedical field, where the number of ontologies and their complexity have increased during the last years. On the other hand, a vast amount of clinical knowledge resides in natural language texts. For these reasons, building and maintaining biomedical ontologies from natural language texts is a relevant and challenging issue. In order to provide a general solution and to minimize the experts' participation during the ontology enriching process, a methodology for extracting terms and relations from natural language texts is proposed in this work. This framework is based on linguistic and statistical methods and semantic role labeling technologies, having been validated in the domain of diabetes, where they have obtained encouraging results with an F-measure of 82.1% and 79.9% for concepts and relations, respectively.

Semi-automated development of conceptual models from natural language text