NLPIR SEMINAR Y2018#9
INTRO
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.
Arrangement
This week’s seminar is organized as follows:
1. The seminar time is 1.pm, Wed, at Zhongguancun Technology Park ,Building 5, 1306.
2. The lecturer is Yaofei Yang, the paper’s titles are An Unsupervised Method for Uncovering Morphological Chains and From Segmentation to Analyses A Probabilistic Model for Unsupervised Morphology Induction.
3. The seminar will be hosted Wang Gang.
4. Attachment is the paper of this seminar, please download in advance
Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper
An Unsupervised Method for Uncovering Morphological Chains
Karthik Narasimhan Regina Barzilay Tommi Jaakkola
Abstract
Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and wordlevel features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.
From Segmentation to Analyses:
A Probabilistic Model for Unsupervised Morphology Induction
Toms Bergmanis Sharon Goldwater
Abstract
A major motivation for unsupervised morphological analysis is to reduce the sparse data problem in under-resourced languages. Most previous work focuses on segmenting surface forms into their constituent morphs (e.g., taking: tak +ing), but surface form segmentation does not solve the sparse data problem as the analyses of take and taking are not connected to each other. We extend the MorphoChains system (Narasimhan et al., 2015) to provide morphological analyses that can abstract over spelling differences in functionally similar morphs. These analyses are not required to use all the orthographic material of a word (stopping: stop +ing), nor are they limited to only that material (acidified: acid +ify +ed). On average across six typologically varied languages our system has a similar or better F-score on EMMA (a measure of underlying morpheme accuracy) than three strong baselines; moreover, the total number of distinct morphemes identified by our system is on average 12.8% lower than for Morfessor (Virpioja et al., 2013), a state-of-the-art surface segmentation system.
