https://www.linguisticsweb.org/ 2026-04-24T12:10:57+00:00 https://www.linguisticsweb.org/ https://www.linguisticsweb.org/lib/tpl/starter/images/favicon.ico text/html 2020-10-01T12:23:26+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:srl&rev=1601547806&do=diff Semantic Role Labelling (SRL) Semantic Role Labelling is the process whereby semantic role labels such as AGENT, PATIENT, BENEFACTOR etc. are assigned to sentence elements. The process of semantic role labelling typically builds on other analysis and annotation steps such as tokenization, part of speech tagging, chunking, syntactic parsing etc. text/html 2023-12-05T11:55:24+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_core_nlp&rev=1701773724&do=diff Stanford Core NLP author: Sabine Bartsch, Technische Universität Darmstadt 1 What are the Stanford Core NLP Tools? The Stanford Core NLP Tools subsume a set of the principal Stanford NLP Tools such as the Stanford POS Tagger, the Stanford Named Entity Recognizer, the Stanford Parser etc. in one integrated package together with models for English and a growing number of other languages. The Stanford Core NLP tools automatically generate annotations that are the foundation of many types of ling… text/html 2022-06-17T23:27:55+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_core_nlp_multiple_files&rev=1655501275&do=diff Stanford CoreNLP Tools: Processing multiple files in a directory While annotating a single file is sometimes all you want to do, the typical corpus linguistic annotation task is likely to require the annotation of multiple files. So far, we have explored different sets of annotators for annotating a single file. In order to accomplish this, we have used the switch text/html 2020-10-01T12:23:27+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_core_nlp_server&rev=1601547807&do=diff Running the CoreNLP Tools as a Local Server The StanfordCoreNLP Tools can also be run as a local server. Call the server by means of the following command or put this in a batch file and run it: text/html 2020-10-05T17:43:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_ner&rev=1601912580&do=diff Stanford Named Entity Recognizer author: Sabine Bartsch, Technische Universität Darmstadt tutorial status: under revision to update to Stanford NER 4.0.0 time stamp: 2020-10-05 builds on an earlier version by Michael Hanl Related tutorials text/html 2022-04-19T13:05:50+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_ner_tagger_python&rev=1650366350&do=diff Stanford NER from Python author: Sabine Bartsch, e-mail: mail@linguisticsweb.org [tutorial status: work in progress: extension - 04.2022] This small example illustrates how the Stanford Named Entity Recognizer (NER) can be driven from Python 3: Note that the last two lines of code (line 24-25) illustrate a way of converting the original list of tuples ( text/html 2020-10-05T17:35:16+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_ner_visualisation&rev=1601912116&do=diff Stanford Named Entity Recognizer author: Sabine Bartsch, Technische Universität Darmstadt tutorial status: under revision builds on an earlier version by Michael Hanl Related tutorials * Stanford NER from Python 1 What is the Stanford NER and what are named entities? text/html 2021-12-12T12:05:11+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_parser&rev=1639307111&do=diff Stanford Parser 1 What is the Stanford Parser? The Stanford Parser is a statistical natural language parser from the Stanford Natural Language Processing Group. It is used to parse input data written in several languages such as English, German, Arabic and Chinese it has been developed and maintained since 2002, mainly by Dan Klein and Christopher Manning. The application is licensed under the GNU text/html 2022-05-14T16:22:46+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_pos_tagger&rev=1652538166&do=diff The Stanford POS Tagger author: Sabine Bartsch, Technische Universität Darmstadt Tutorial builds on software and input from the Stanford PoS Tagger website. Related tutorial: Stanford PoS Tagger: tagging from Python 1 What is the Stanford PoS Tagger? The Stanford PoS Tagger is a probabilistic Part of Speech Tagger developed by the Stanford Natural Language Processing Group. It is widely used in state of the art applications in natural language processing. text/html 2023-12-05T11:17:39+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_pos_tagger_python&rev=1701771459&do=diff Stanford PoS Tagger: tagging from Python author: Sabine Bartsch, e-mail: mail@linguisticsweb.org [tutorial status: last change - 2023-12-05] Related tutorial: Stanford PoS Tagger While we will often be running an annotation tool in a stand-alone fashion directly from the command line, there are many scenarios in which we would like to integrate an automatic annotation tool in a larger workflow, for example with the aim of running pre-processing and annotation steps as well as analyses in on… text/html 2020-10-01T12:23:28+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:stanford_word_segmenter&rev=1601547808&do=diff Stanford Word Segmenter The Stanford Word Segmenter is a piece of software that can automatically segment text into words. It is is designed to handle non-Indo-European languages such as Arabic and Chinese which pose special segmentation / tokenization challenges. text/html 2025-02-12T23:13:57+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.linguisticsweb.org/doku.php?id=linguisticsweb:tutorials:automaticannotation:treetagger&rev=1739398437&do=diff TreeTagger TUTORIAL STATUS: workable, subject to minor additions and modifications, mostly examples for further languages [2025-01-29] TreeTagger version 3.2.3 Languages: English, German, Middle High German, Korean and many others, check models on the TreeTagger website for further language models.