Technologies for NLP Clause Samples

Technologies for NLP. In the case of NLP, there are multiple libraries and toolkits that deal with a different variety of techniques. Some of the most popular ones are: ● Nltk25 (Apache License v2), developed in Python, is used for NLP common tasks, such as tagging, tokenizing, and stemming. ● Stanford CoreNLP26 (GNU General Public License v3+) is a popular NLP toolkit for Java. Apart from the common NLP tasks, it supports advanced processing such as named entity recognition, dependency analysis, and part-of-speech tagging. This toolkit is still maintained by the Stanford NLP group. ● OpenNLP27 (Apache License v2) is a Java API that supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. ● Word2Vec28 (Apache License v2) is an algorithm developed by (▇▇▇▇▇▇▇ 2013) at Google to assign arbitrary-length vectors to words that correspond to the meaning of the word. The vectors created depend on the corpus used, therefore it is important to know which corpus was used when working with a set of word vectors. It is important to highlight here the fact that OpenReq project will not only deal with English language, but also with Italian and German, since the telecom trial deals with text written in the Italian language and, for the case of Siemens, with text (partially) written in the German language. This diversity of languages used to write the text analyzed is a challenge for the 22 ▇▇▇▇://▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇▇/ 24 ▇▇▇▇://▇▇▇.▇▇.▇▇▇▇▇▇▇.▇▇.▇▇/ml/weka/ 25 ▇▇▇▇://▇▇▇.▇▇▇▇.▇▇▇/ 26 ▇▇▇▇▇://▇▇▇▇▇▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇/CoreNLP/ 27 ▇▇▇▇▇://▇▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇▇/ 28 ▇▇▇▇▇://▇▇▇▇.▇▇▇▇▇▇.▇▇▇/archive/p/word2vec/ project. The majority of the existing NLP approaches target the English language, as they are trained and validated using English text corpora. Although NLP approaches and software libraries exist for the other two languages (Basili 2015) (▇▇▇▇▇▇▇ 2012), their performances (e.g., precision) might be inferior compared to the well-established, English-based ones.
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