Titlebook: Computational Linguistics and Intelligent Text Processing; 17th International C Alexander Gelbukh Conference proceedings 2018 Springer Inte

Insulin

Mining the Web for Collocations: IR Models of Term AssociationsNLP, which feeds into several other tasks (e.g., parsing, idioms, summarization, etc.). Despite this attention the problem has remained a “daunting challenge.” As others have observed before, existing approaches based on frequencies and statistical information have limitations. An even bigger proble

instill

MINT

Description of Turkish Paraphrase Corpus Structure and Generation Methodexible, multipurpose and expandable. Here we describe the steps we took in the development of Turkish paraphrase corpus, the factors we considered, problems we faced and how we dealt with them. Currently our corpus contains nearly 4000 sentences with the ratio of 60% paraphrase and 40% non-paraphras

臭了生气

Extracting Terminological Relationships from Historical Patterns of Social Media Termstweets for example) and then to trace the history of each term. Similar history indicates a relationship between terms. This indication can be validated using further processing. For example, if the term t1 and t2 were frequently used in Twitter at certain days, and there is a match in the frequency

Mundane

Offbeat

刺耳

Culmination

NUL

,Die mehrstufigen Strömungsmaschinen,between these linguistic data formats is impossible since they are increasingly multiplatform and multi-providers. LDF suffer from several communication issues. Therefore, they have to face several interoperability issues in order to guarantee consistency and avoid redundancy. In an interoperability

machination

https://doi.org/10.1007/978-3-662-30213-2provides fundamental Persian text processing steps includes several modules. In developing some modules of the toolbox such as normalizer, tokenizer, sentencizer, stop word detector, and Part-Of-Speech tagger previous studies are applied. In other modules i.e. Persian lemmatizer and NP chunker, new