Joint Semantic Synthesis and Morphological Analysis of the Derived Word
Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+able+ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word's meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituents segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DerivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist's notion of morphological productivity.
Code (0)
등록된 구현이 없습니다.
Tasks
Additive modelsMorphological AnalysisSimilar Papers 제목 키워드 기반
Word Segmentation and Morphological Parsing for Sanskrit
We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task as a sequence labelling task by predicting edit operations from which…
Morphological AnalysisSegmentationA Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis
Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic str…
Dependency ParsingMorphological AnalysisMorphological TaggingSegmentation+1Improving Word Embeddings Using Kernel PCA
Word-based embedding approaches such as Word2Vec capture the meaning of words and relations between them, particularly well when trained with large text collections; however, they fail to do so with small datasets. Exten…
ArticlesSentenceSentence ClassificationWord Embeddings+1Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics
Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew
In standard NLP pipelines, morphological analysis and disambiguation (MA{\&}D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphol…
Dependency ParsingMorphological Analysis