Sam Hardwick


Akkadian Treebank for early Neo-Assyrian Royal Inscriptions
Mikko Luukko | Aleksi Sahala | Sam Hardwick | Krister Lindén
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories


Extracting Semantic Frames using hfst-pmatch
Sam Hardwick | Miikka Silfverberg | Krister Lindén
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)


HFST-SweNER — A New NER Resource for Swedish
Dimitrios Kokkinakis | Jyrki Niemi | Sam Hardwick | Krister Lindén | Lars Borin
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).


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Effect of Language and Error Models on Efficiency of Finite-State Spell-Checking and Correction
Tommi A Pirinen | Sam Hardwick
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing