Abstract: This new version of Termout has many functions for automatic and manual terminology processing. An example of computer assisted terminology, it lets you work with your specialized corpus (for now, only in English and Spanish), to extract terms from it, to evaluate the extracted candidates, to classify them in semantic categories, extract definitions from the corpus, extract equivalents in the other language, obtain synonyms (term variants) and even more. Of course, it also has functions for terminology database management, and lets you import an export your database in CSV, HTML and TBX standards.

The URL is:
http://www.termout.org
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

Contents

I. Introduction

II. Corpus management

1. Create corpus
2. Manage corpus
3. Language detection
4. POS tagging
5. Representativity
6. Indexing

III. Terminology extraction

5. Extract terms
6. Evaluate results

IV. Information extraction / enrichment

9. Concordances
10. Semantic categories
11. Text mining
12. Bilingual alignment
13. Variants

V. Database management

13. Manage
14. Export
15. Import

Credits

Comments



* * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

I. Introduction
Return

Substantial development has taken place in the field Computer-assisted Terminology Processing in the last decades, and terminology professionals are now better equipped to engage in the task of glossary creation. That being said, there is still ample room for improvement. Firstly, there is no tool in the market that can integrate solutions for the different requirements of a terminology project. Secondly, the software currently available for terminology management is time consuming because programs demand repetitive tasks that in many cases could be automatized, and at least a number of these could be addressed by current advances in Natural Language Processing. This would be convenient not only to improve productivity but also to protect users from unnecessary stress and even physical harm due to the time spent sitting in front of a computer and the incessant mechanical repetition of the same hand movements.

In this context, we present an ambitious lexicographic project: to automatize, as much as possible, the different tasks involved in the creation of a specialized dictionary. It is specifically aimed to be used in cases where terminology-related professionals need to generate raw material which they can later improve manually by adding or correcting data, especially in cases where time is of essence.

The program offers solutions for the complete sequence of tasks of a specialized dictionary project, i.e., the tasks of corpus processing (file upload, conversion to plain-text format, language detection, POS-tagging and indexing), terminology extraction (with optional human supervision), information extraction (definitions, equivalence in another language, hyperonymy, synonymy, etc.) and database management (editing, storage, retrieval and export~import options in HTML, CSV and TBX).

The idea is to execute all the tasks in the suggested order, although some users may prefer to do otherwise. For instance, some may instead prefer to upload their own material and use the tool to complement it with information extracted from the corpus. For instance, one could think of a user that has a specific list of terms (or a TBX dictionary) and only wants to extract definitions or equivalences. In the following lines we describe the functions that have been already implemented in the program.

Clarification: We are not a commercial enterprise. We are doing this just out of passion only, because we are terminologists and we love our work.

Disclaimer: You are the only responsible for the safety of your data (corpus and/or database). Remember to back it up periodically.

II. Corpus management

1. Corpus creation: There are currently two options to create a corpus with Termout. The first one is to download documents from the web (this is a new function: February 11, 2026).
   The idea here is to provide a single URL so the program can inspect and see if there are any interesting patterns in the links contained in said document. The best strategy is to select the ``Archive'' pages of publications, i.e., those that concentrate the links to download documents.
   Keep in mind that not all web pages allow bots to download documents. In fact, these denials are happening with increasing frequency.
   The second option is to upload your own corpus. Termout can handle almost every file format there is: txt, pdf, ps, doc, docx, odt, and all html and xml related formats. The user only needs to create a zip file with the corpus and use this upload function. Termout will inflate the zip and analyze each file in the contents. It will guess the file format and convert it to UTF-8 plain-text. The ideal corpus to analyze with Termout is a scientific journal with at least some 300 texts. Anything less than that would be insufficient for the kind of algorithms that are in place. Figure 1 shows a screenshot of the file-uploading function.
   
   
   Figure 1: Uploading documents
   
   
   
2. Manage corpus: These functions allow the user to modify the corpus by deleting files or changing their names manually. As shown in Figure 2, users can select files individually or as a group. The language and size of the documents are also provided by the program.
   
   
   Figure 2: Management of the uploaded files
   
   
   
3. Language detection: It will also detect the language of the documents and even the different fragments of text inside a document. Every text that is not recognized as either English of Spanish will be deleted. At the moment the program only works with these two languages, but we plan to incorporate others in the future.
   
   
4. POS tagger: Once the corpus has been uploaded, this function applies a POS-tagger to the texts. This may take a long time, depending on the corpus' size. It's important to let the program work. However, if the connection is for some reason interrupted, then the user can safely refresh the page: nothing will be lost. The program will continue the job where it was left.
   
   
5. Representativity: This function is intended for the calculation of the representativity of the sample based on the plot of vocabulary grow (Figure 3), which is obtained using only single-word nouns. One can consider that a sample is representative of the vocabulary when the curve of vocabulary grow begins to show saturation.
   
   Figure 3: Plot of the vocabulary grow function
   
   In order to help users visualize the point of saturation of the curve, Termout also presents the derivative of the vocabulary grow function (Figure 4). This new chart shows the ``diminishing returns'' one gets by increasing the sample size, i.e., one receives less and less new data, up to a point in which the effort put in increasing the sample is no longer justified.
   
   Figure 4: Derivative of the vocabulary grow function
   
   
6. Indexing: This function is necessary for the fast retrieval of concordances, and it is what makes Termout a robust tool to work with large corpora.

III. Terminology extraction
Return

7. Extract terms: This is Termout's main function: to separate the lexical units in the corpus in the categories of terms and non-terms. Termout does not use any form of external resource for this task. All the input for the process is the same analyzed corpus, which is subjected to a series of statistical procedures. Again, this can take considerable time to finish, depending on the size of the corpus.
   
8. Evaluate results: Once the terminology extraction process is finished, users have the possibility to evaluate results. They are presented with two lists because the terms are also separated by language. In both cases, they are presented in alphabetical order and each unit has a checkbox to allow users to move units from one list to the other (be careful: once you delete a term, there is no way back). Figure 5 shows a case with a corpus of linguistics journals with text in English and Spanish.
   
   
   Figure 5: Evaluating the results of the term extraction process
   

IV. Information extraction / enrichment
Return

9. Concordances: As usual in this type of programs, Termout also includes a function to retrieve concordances of an expression in the corpus, which can be a single or multiword form.
10. Semantic categories: Once the terminology extraction process is finished and the results have been evaluated, users can start to apply the information extraction functions. The first one is the extraction semantic categories. The result is a hierarchic graph of the extracted terms (Figure 6).
    
    
    Figure 6: Semantic graph of the terms
    
    In any case, there are two options: 1) to process all terms in the database or 2) to process only a particular term.
    
11. Text mining: For each term in each language, the program will also attempt to extract contexts of occurrence of the terms in the corpus which may contain useful information for the elaboration of a definition. Figure 7 shows how, for each term in the database, some definitions are presented, highlighting important segments.
    
    
    Figure 7: Extraction of definitions from the corpus
    
    
    
12. Bilingual alignment: Termout will attempt to align the lists of terms in each language by using a battery of statistical measures. It is not necessary to provide Termout with a parallel corpora, but ideally one should have documents that have at least fragments of text in another language. This is the case, of course, with scientific journals written in Spanish: they usually have fragments like the title, abstract and keywords in English, as well as a variable number of bibliographic references in this language. Termout takes advantage of the co-occurrence of words of the two languages in the same documents and uses it to compute a series of association measures which also include an orthographic similarity coefficient for the cognates. Figure 8 shows some examples of the alignments. In cases when there is not much certainty, the program also presents some segments where equivalent candidates co-occur, so users can decide for themselves whether the proposed candidate should be retained or rejected.
    
    
    Figure 8: Bilingual alignment of the terms
    
    
    
13. Variants: Once the bilingual terminology alignment has finished, a list of possible synonyms or term variants are presented to the users. Termout considers that two terms in the same language are variants when they have the same equivalent in the other language. Again, results are not perfect. This function, in particular, is affected by cases of polysemy in one of the languages.

V. Database management
Return

14. Manage: As usual in terminology management software, Termout offers different options for manually editing the terminology database. Users can create new term records, they can destroy them or modify them at any moment.
    It's important to take into account that the database contains term records because in this program a terminology record is assigned to a term, not to a concept (i.e., it is a semasiological and not an onomasiological database). As in every terminology database, each record has a number of fields, such as grammatical category, gender, definitions, equivalents in another language, examples of use, among others.
15. Export: Once the glossary creation is finished and everyone is satisfied with the result, the program will export the glossary in three different standards: CSV, HTML and TBX.
16. Import: Here the user is expected to present a glossary in the TBX standard and Termout will import it to its database, merging it to an existing glossary if any.

Credits: This program was designed and coded by Rogelio Nazar, with the help of Nicolás Acosta for programming. Lexicographers Irene Renau and David Lindemann are acting as advisers, and as beta-testers we have Ana Castro and Benjamín López. Sebastián Márques is our graphic designer, although here we made a mess of his design (it is in the to-do list to restore it to its original splendor). We are the Tecling Group. For more details and other products, check our website: http://www.tecling.com

Comments: If you found a bug or have something to say, please send email to rogelio dot nazar at gmail dot com.