The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguisticanalysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
As shown in the previous section, detecting news plagiarism is straightforward and easy, especially as the media go increasingly online. However, more sophis- ticated techniques are required when news pieces are plagiarised from other lan- guages by journalists, who tend to translate the text freely into another language (usually, their mother tongue) — often using machine translation services, such as Google Translate. In these cases, the output of the machine translation is fre- quently grammatically flawed. To a lesser or greater extent, adjustments are therefore required, not only to make the text readable, but also publishable. In order to raise the suspicion that a text derives from an original in another lan- guage, and consequently detect instances of plagiarism of this type (as is the case of Público discussed above), it is necessary to either rely on intuition (the feeling of déjà-vu), or else build upon linguisticanalysis. The latter is also required to provide evidence of the lifting, as the former is insufficient in this respect.
Šio straipsnio tikslas – nustatyti moralės modelius, analizuojant konceptualiąją metaforą politiniame diskurse anglų ir lietuvių kalbose. Remtasi analitiniais straipsniais politikos temomis. Jie pasitelkti iš Interneto tinklalapių www.politika.lt ir www.economist.com elektroninio archyvo. Straipsniai anali- zuojami remiantis kognityvinės lingvistikos principais bei kokybiniu analizės metodu (Fauconnier & turner 2002; Kövecses 2005; Lakoff & Johnson 1997, Lakoff 2005; turner 1994), kurie leidžia atskleisti kalbiniuose pasakymuose (linguistic expressions) glūdinčias konceptualiąsias metaforas. Kalbiniai pasakymai buvo skirstomi pagal tris moralinio vertinimo modelius: (1) Huxley, kuris apibrėžia moralę kaip žmonių sąmoningai sukurtą vertinimo skalę, reikalingą kovoje su žmogaus įgimtomis blogybėmis, (2) kanto požiūris į moralę, kaip į racionalų veiksmą glaudžiai siejamą su pareigomis, bei galiausiai (3) integruotas požiūris, kai moralus elgesys suvokiamas kaip sentimentų ir emocinių reakcijų pasekmė (Black 1995; Boehm 2000; Flack & de Waal 2002). Išanalizavus konceptualiųjų metaforų kalbinę raišką paaiškėjo, kad straipsniuose vyrauja metaforos PoLITIKa KAIP JĖGA ir PoLItIKA KAIP tARPUSAVIo SANtyKIAI.
In the field of demographics, most studies use linguisticanalysis in order to extract use- ful features for predicting demographic information as gender, race, and age. Burger et al.  produce ngrams from users’ tweets, description, screen name, and full name, in order to predict Twitter user gender. They conclude that the training of an SVM classifier with the combination of all factors can create an efficient and accurate prediction scheme (92% accuracy) for gender classification. Also, Chen et al.  introduce a similar methodology for predicting gender, ethnicity, and age. However, using ngrams from the social neighbors, including followers and friends, and the dis- tribution of 100 generated topics of LDA algorithm as the input of SVM classifier. In their results, the performance of classification is much lower in terms of ethnicity and age. Gilbert et al.  present an interesting statistical overview in Twitter and Pinterest using textual analysis and comparing what users write on Pinterest to what write text in Twitter. Cunha et al.  used Twitter data to analyze the difference between males and females in terms of generation of hashtags. Their results emphasize gender as a factor that influences the user’s choice of specific hashtags to a specific topic.
One reason why descriptive work is often viewed as atheoretical is that it is not explanatory. Both formal work and functionalist work in recent decades have focused on explanation, and the notion of theory has become entwined with that of explanation, so that for many linguists, a theoretical question is understood as one which involves explanation. The nature of functionalist explanation is such that it is external to the grammar, not only in the sense that the theoretical concepts appealed to in the explanation lie outside of grammar, but also (and perhaps more controversially) because there is no way to build these explanations into the grammar itself. Under the view of this paper, functional explanations - explanations for why languages are the way they are - apply primarily at the level of language change. Functional factors or motivations exert “force” on linguistic change, encouraging certain changes and discouraging others. Such functional factors sometimes work together and sometimes work at odds with each other, in which case we have competing motivations (Haiman 1983, 1985; Croft 1990). But this competition is resolved at the level of language change: once one motivation “wins”, then that is the way the language is, and when speakers learn the language, they learn the language that is the result of functional factors without any awareness of them, either conscious or unconscious. The grammatical knowledge that underlies their linguistic behaviour is the immediate explanation for their linguistic behaviour. There may be functional explanations for many features of the grammar, but these features are established before the speaker ever learns the language and long before they use it. Grammars are in this sense an emergent phenomenon, not in the sense of Hopper (1987), according to which grammars are always emerging, not crystalizing, but in the sense that the particular way these factors resolve themselves is not reducible to these explanatory factors (or a ranking of these factors), and in the sense that the grammar takes on a life of its own, so to speak, above and beyond the functional principles that have shaped it. A grammatical description of a language is thus not deficient or inadequate if it leaves out explanations for why the language is the way it is. In fact, in so far as grammars exist independently of explanation, there is a need for description independent of explanation.
Linguistic students have deep knowledge of natural language so they produce good formal specifications but have serious difficulties in translating their knowledge to computer models, since they lack Computer Science skills. Hence the need to create a tool that facilitates this process. To use PAG, in addiction to writing the specification for the language, the user has to type some information in a user interface: the sentence he wants to analyze (it can be uploaded or written) and the values of the inherited attributes. Then this information is processed and the program makes all the decorated parse trees possible (there may be more than one) and notifies the user of all the errors (if any) that occurred. Students find the decorated parse trees very useful to understand ambiguity of sentences, since it allows them to see a table that shows attribute values for each node. Also, each entry of this table links to the corresponding semantic equation used to calculate the attribute. Notwithstanding that this tool solves some of the problems this group of students and teachers faced, we feel that we can improve this solution by making the specification of the rules even simpler and a much user friendlier appealing user interface including animated visualizations.
6. In the tweets presented below for your analysis, at least one of the choices mentioned in open-ended question is correct according to our interpretation. Nevertheless, it is worth mentioning that irony is a highly subjective device and the opinion of our manual annotation may not correspond to the evaluator´s one. Thus, the category "OTHER" "NO FIGURATIVE DEVICE INVOLVED" "NONE OF THE ABOVE" provides a certain degree of freedom to annotator to specify his/her subjective opinion what figurative device has been used in one or the other tweet with observations if necessary.
I will not defend Dummett’s theses (I) and (II). I agree that there is a division in the philosophical labour between metaphysicians and phi- losophers of language, and that the philosophy of language does not have a foundational role in respect of other philosophical fields. I also agree that the Manifestation Argument can be blocked by rejecting the con- stitutive constraint. However, I will argue that there is a constraint that makes the link between linguistic competence and semantics more inti- mate than some philosophers believe. I take this constraint to be part of Dummett’s legacy in the philosophy of language. I will address the point by discussing Cappelen and Lepore’s criticism of Incompleteness Argu- ments. I will claim that despite the fact that they recognize a division in the philosophical labour between metaphysicians and philosophers of language, their criticism of Incompleteness Arguments is mistakenly grounded on an underestimation of the connection between linguistic competence and semantics. 9
We will discuss manually compiled lexicons such as the General Inquirer (Emotions, or Evaluative adjectives), computationally enriched (manually compiled) ontologies such as Valitutti et al. (2004) or Pasqualotti & Vieira (2008), lexicons computed from corpora (Riloff et al. 2003, Xu et al. 2010, Pitel & Greffenstette, 2008), or obtained through other kinds of methods (Silva et al. 2012, Staiano & Guierini 2014). We will present annotated corpora with emotions and/or opinions while discussing some linguistic problems for identifying, and classifying, emotion in text, from Bruce & Wiebe (1999), Pang & Lee (2008), Volkova et al. (2010) and others.
The development of industry and business caused the explosion of marketing and advertising. The power of the media and technology does not recognize national boundaries. The English language is a global language, lingua franca (Crystal.D:1997:9) spoken by nearly 450 million speakers in 115 countries around the world. Being so dominant, the English language brings new ideas into foreign societies. Due to such an influence, communities inevitably begin to enrich their own linguistic experience which obviously changes the way people think and form their attitudes towards the surrounding world. Millions of people study the English language in order to be able to communicate with others when travelling, whether for business or for international relations. Italians can communicate with Russians in Portugal through the English language, and so on.
The lexical variation in a text is noticed by a human reader but, in an automatic processing, the variants are treated as autonomous forms when, in fact, they are relat- ed, linked by the history of language. The example of the forms giolho, geolho, jo- elho, and juelho, all used during the classic period of Portuguese, present, clearly, a good example of this linguistic variation. The search of each of these forms in Corpus Lexicográfico do Português produces interesting results, showing the lexicographical and textual registration of forms across time.
Languages in contact habitually tend to incorporate lexical borrowings from the most influential one to the other. This linguistic device has produced an important terminological confusion in Spanish. Most authors prefer the term préstamo to refer to an elemento lingüístico (léxico, de ordinario) que una lengua toma de otra, bien adoptándolo en su forma primitiva, bien imitándolo y transformándolo más o menos (Lázaro Carreter, 1990: 333). Others consider the term préstamo inaccurate and have coined the alternative trasplante or préstamo extranjero (Casas, 1986: 163), as well as adopción lingüística or aportación lingüística. 3 The different degrees of
Results’ analysis show that, when using syntactic in- formation, structured representations (syntactic-tree and sequence-of-words) harm the learner. Further more, as ini- tially expected, sentence syntactic structure does not prop- erly reveal document class; it could perhaps expose docu- ment writer or the kind of used language (like generic as the newspaper documents vs. specific areas of knowledge like medical or juridic ones).