Microsoft Word Romanian Dictionary Download !!TOP!!

With Enagramm Spellchecker Add-in you can check spelling for Abkhazian, Adyghe, Albanian, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Croatian, Czech, Estonian, Georgian, Greek, Kazakh, Kyrgyz, Latvian, Lithuanian, Macedonian, Ossetian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Tatar, Tajik, Turkmen, Turkish, Ukrainian, Uzbek Languages. It also features a personal dictionary for words that you might commonly use but that are not found in a conventional dictionary.

A custom dictionary contains words that are not in the main Office dictionary. You can either create a new dictionary or modify the main dictionary. Custom dictionaries created in Word are shared with the other Office programs. This document contains instructions for creating and editing a custom dictionary using Word.

Microsoft Word Romanian Dictionary Download

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You have the option to add you own words to the workspace dictionary. The easiest, is to put your cursoron the word you wish to add, when you lightbulb shows up, hit Ctrl+. (windows) / Cmd+. (Mac). You will get a listof suggestions and the option to add the word.

As with other languages, the acute accent is sometimes used in Romanian texts to indicate the stressed vowel in some words. This use is regular in dictionary headwords, but also occasionally found in carefully edited texts to disambiguate between homographs that are not also homophones, such as to differentiate between cpii ("copies") and copi ("children"), ra ("the era") and er ("was"), cele ("the needles") and acle ("those"), etc. The accent also distinguishes between homographic verb forms, such as ncie and ncui ("he locks" and "he has locked").[27]

Kindle allows you to perform various functions on selected text, including obtaining a dictionary definition, adding notes and highlights, copying the text to the clipboard and searching the web.To do this, first select text as you normally would in browse mode; e.g. by using shift and the cursor keys.Once you have selected text, press the applications key or shift+f10 to show the available options for working with the selection.If you do this with no text selected, options will be shown for the word at the cursor.

The creation of the default LIWC2015 dictionary was a laborious process of several years, which led to a list of 6549 words, word stems, and emoticons assigned to approximately 90 higher- and lower- level categories, based on psychometric standards (for a thorough presentation of each development stage, see Pennebaker et al. 2015). The validity and reliability methods that determined the composition of LIWC2015 are one of the main reasons why LIWC2015 is a powerful resource (Boyd 2017). Furthermore, whilst other dictionary-based tools are more specialized, LIWC2015 covers a variety of features, including four structural linguistic dimensions, 21 parts of speech and other function words, 41 categories with psychological connotation, six types of personal concerns, five forms of informal language, and four summary variables (analytical thinking, clout, authenticity, and emotional tone). The summary variables are not available for translation; they remain unique features of the English version. The comprehensive list of LIWC2015 categories displayed hierarchically, with examples, is provided by Pennebaker et al. (2015).

The LIWC2015 software supports various machine-readable formats of the input text and demonstrates flexibility in the options that the user can choose to investigate the linguistic contents of interest. By operating a user-friendly menu, the researcher can instantly compare each target word of each uploaded text file, with the dictionary words. A target word is part of the text introduced in the software for analysis, whereas a dictionary word belongs to the LIWC2015 dictionary. Every time the software finds a match, an item for the category or categories attached to the dictionary word is counted. Moreover, as the target file is crossed, other structural elements of the text such as punctuation or the total number of words are also recorded (Pennebaker et al. 2015). Hence, the LIWC2015 processor acts like a tokenizer and word counter; it can calculate frequencies adjusted by the total number of words and display them as percentages. Although the word counting approach can occasionally lead to incorrect classifications since a system like LIWC2015 could not detect sarcasm or semantic nuances, it is generally efficient because people naturally tend to express themselves using words grouped into meaningful clusters (Boyd 2017). Thus, usually, if a target word is misclassified, other related words would compensate for the same dictionary category.

The process of translating tools like LIWC is not straightforward since every language has specific grammar rules and semantics (e.g., Levshina 2016; Patard 2014) that need to be accounted for in order for the software to reveal accurate results. The biggest challenge is to decide what translations and word variations to include in the dictionary and what categories to attach to specific words when there are language inconsistencies such as changes in meanings due to translation, or different ways to form verb tenses, distinguishing between masculine and feminine words, articulating words, or dealing with diacritics. Other authors discussed such adaptation issues in the context of developing, for example, the Spanish LIWC2001 (Ramrez-Esparza et al. 2007), the French LIWC2007 (Piolat et al. 2011), or the Dutch LIWC2007 (Boot et al. 2017). If we analyze the existent LIWC versions, we notice that a different dictionary has emerged with every translation. In this regard, based on the files downloaded from the dictionaries.liwc.net webpage, the Spanish LIWC2001 (Ramrez-Esparza et al. 2007) contains 12,656 words, the French LIWC2007 (Piolat et al. 2011) contains 39,164 words, the Italian LIWC2007 (Agosti and Rellini 2007) contains 5153 words, and the Dutch LIWC2007 (Boot et al. 2017) contains 11,091 words. However, even though LIWC versions differ in length, they tend to generate results consistent with those obtained with the English version, and also to show good validity, as shown in the papers dedicated to presenting them, which we have already cited. In other words, translation challenges tend not to be a significant obstacle.

The process of developing the Romanian LIWC2015 took one year and a half and involved three main steps. First, the 6539 words of the English dictionary were equally assigned to six translators, and a first draft of the Romanian dictionary was obtained. This first draft contained up to five synonyms for every English word, without any adjustments to the categories. The translators held periodic meetings to discuss the problems they encountered throughout the process, how to solve them, and whether the translation procedure should be refined. Each word was translated from English to Romanian using several dictionaries. In the second phase of the development of Ro-LIWC2015, the first author revised all the translations, following the same procedure as in the first step. At this point, every word was assigned the appropriate categories according to the Romanian grammar and semantics while keeping the duplicates. Finally, all files containing the second Romanian draft were copied in a single file. Then, the duplicates were marked automatically using a function in Microsoft Excel and removed manually. If the duplicates had different categories, those categories that stood out were assessed in terms of whether they should be kept or not, retaking the same steps as before. Specifically, we rechecked the definitions of the LIWC2015 words and their translations using several dictionaries and relying on our grammar and semantics knowledge as native Romanian speakers. In general, the categories were merged, given that this step was more of a chance to detect any mistakes that might have slipped. The translation protocol had to include several specific rules derived from the language differences between English and Romanian. More details about the translation procedure are available in Supplementary Material 1. The final Ro-LIWC2015 contains 47,825 entries altogether, but not all of these entries represent unique words because some words were spelled in two different forms, with or without diacritics.

One of the advantages of using a dictionary with a higher number of entries is that the researchers could detect the meaning of more words from the input text according to the predefined labels of the dictionary. For instance, as Meier et al. (2018) showed, the German version of the dictionary, DE-LIWC2015, which comprises 18,000 words and 77 categories, captured 87.84% of the total words in the analyzed text. In contrast, the German version of LIWC2001 (Wolf et al. 2008), which contains only 7598 words and 68 categories, detected only 70% of the same input text (Meier et al. 2018).

Throughout both approaches, we focus only on the lower-level features of the LIWC2015 dictionary, given that the hierarchically superior ones represent the cumulative percentage of the constituent categories. For example, affect is a higher-level category comprising positive emotions and negative emotions, which means that it yields values equal to the sum of the word percentages for the two valence features. Furthermore, sadness, anxiety, and anger categories are subordinated to negative emotions. Therefore, for instance, for the supervised learning approach, we included in the model only the positive emotions category, which does not have subcomponents, along with sadness, anxiety, and anger, while excluding the affect and negative emotions categories. In Table 1, the higher-level features are aligned to the left and the lower-level ones are indented.

The procedure to transform the words found in the selected books into data was straightforward. The English version of the books was processed with the English version of the LIWC2015 dictionary, whereas the Romanian version of the same books was processed with Ro-LIWC2015. Data is available on our Open Science Framework account (Sava and Dudu 2020). ff782bc1db