Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in low-resource languages. In addition (as shown here), in a low-resource setting, a lemmatizer can learn more from $n$ labeled examples of distinct words (types) than from $n$ (contiguous) labeled tokens, since the latter contain far fewer distinct types. To combine the efficiency of type-based learning with the benefits of context, we propose a way to train a context-sensitive lemmatizer with little or no labeled corpus data, using inflection tables from the UniMorph project and raw text examples from Wikipedia that provide sentence contexts for the unambiguous UniMorph examples. Despite these being unambiguous examples, the model successfully generalizes from them, leading to improved results (both overall, and especially on unseen words) in comparison to a baseline that does not use context.
Code (1)
Tasks
Data AugmentationLEMMALemmatizationSentenceSimilar Papers 제목 키워드 기반
Context Sensitive Neural Lemmatization with Lematus
The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words. Yet previous systems have not carefully evaluated whether the use of context actually helps in thes…
DecoderLemmatizationMachine TranslationSentence+1Training Data Augmentation for Context-Sensitive Neural Lemmatizer Using Inflection Tables and Raw Text
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches re…
Data AugmentationLEMMALemmatizationSentenceContext Aware Lemmatization and Morphological Tagging Method in Turkish
The smallest part of a word that defines the word is called a word root. Word roots are used to increase success in many applications since they simplify the word. In this study, the lemmatization model, which is a word …
LemmatizationMorphological TaggingUniversal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
In this paper we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method, our context-sensitive lemmatizer gener…
Data AugmentationLEMMALemmatizationA Simple Joint Model for Improved Contextual Neural Lemmatization
English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known a…
LEMMALemmatizationMorphological Tagging