Are written corpora useful for phonological research? Word frequency lists for low-resource languages have become ubiquitous in recent years [@Crubadan]. For many languages there is direct correspondence between their written forms and their alphabets, but it is not clear whether written corpora can adequately represent language use. We use 15 low-resource languages and compare several information-theoretic properties across three corpus types. We show that despite differences in origin and genre, estimates in one corpus are highly correlated with estimates in other corpora.
more »
« less
The Natural Stories corpus: a reading-time corpus of English texts containing rare syntactic constructions
Abstract It is now a common practice to compare models of human language processing by comparing how well they predict behavioral and neural measures of processing difficulty, such as reading times, on corpora of rich naturalistic linguistic materials. However, many of these corpora, which are based on naturally-occurring text, do not contain many of the low-frequency syntactic constructions that are often required to distinguish between processing theories. Here we describe a new corpus consisting of English texts edited to contain many low-frequency syntactic constructions while still sounding fluent to native speakers. The corpus is annotated with hand-corrected Penn Treebank-style parse trees and includes self-paced reading time data and aligned audio recordings. We give an overview of the content of the corpus, review recent work using the corpus, and release the data.
more »
« less
- Award ID(s):
- 1947307
- PAR ID:
- 10259957
- Date Published:
- Journal Name:
- Language Resources and Evaluation
- Volume:
- 55
- Issue:
- 1
- ISSN:
- 1574-020X
- Page Range / eLocation ID:
- 63 to 77
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Generating a high-quality explainable summary of a multi-review corpus can help people save time in reading the reviews. With natural language processing and text clustering, people can generate both abstractive and extractive summaries on a corpus containing up to 967 product reviews (Moody et al. 2022). However, the overall quality of the summaries needs further improvement. Noticing that online reviews in the corpus come from a diverse population, we take an approach of removing irrelevant human factors through pre-processing. Apply available pre-trained models together with reference based and reference free metrics, we filter out noise in each review automatically prior to summary generation. Our computational experiments evident that one may significantly improve the overall quality of an explainable summary from such a pre-processed corpus than from the original one. It is suggested of applying available high-quality pre-trained tools to filter noises rather than start from scratch. Although this work is on the specific multi-review corpus, the methods and conclusions should be helpful for generating summaries for other multi-review corpora.
-
more » « less
Abstract We present CELER (Corpus of Eye Movements in L1 and L2 English Reading), a broad coverage eye-tracking corpus for English. CELER comprises over 320,000 words, and eye-tracking data from 365 participants. Sixty-nine participants are L1 (first language) speakers, and 296 are L2 (second language) speakers from a wide range of English proficiency levels and five different native language backgrounds. As such, CELER has an order of magnitude more L2 participants than any currently available eye movements dataset with L2 readers. Each participant in CELER reads 156 newswire sentences from the Wall Street Journal (WSJ), in a new experimental design where half of the sentences are shared across participants and half are unique to each participant. We provide analyses that compare L1 and L2 participants with respect to standard reading time measures, as well as the effects of frequency, surprisal, and word length on reading times. These analyses validate the corpus and demonstrate some of its strengths. We envision CELER to enable new types of research on language processing and acquisition, and to facilitate interactions between psycholinguistics and natural language processing (NLP).
-
Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. Moreover, there is a dearth of annotated corpora for investigating the cross-linguistic variation of adposition semantics, or for building multilingual disambiguation systems. This paper presents a corpus in which all adpositions have been semantically annotated in Mandarin Chinese; to the best of our knowledge, this is the first Chinese corpus to be broadly annotated with adposition semantics. Our approach adapts a framework that defined a general set of supersenses according to ostensibly language-independent semantic criteria, though its development focused primarily on English prepositions (Schneider et al., 2018). We find that the supersense categories are well-suited to Chinese adpositions despite syntactic differences from English. On a Mandarin translation of The Little Prince, we achieve high inter-annotator agreement and analyze semantic correspondences of adposition tokens in bitext.more » « less
-
Digital pathology is a relatively new field that stands to gain from modern big data and machine learning techniques. In the United States alone, millions of pathology slides are created and interpreted by a human expert each year, suggesting that there is ample data available to support machine learning research. However, the relevant corpora that currently exist contain only hundreds of images, not enough to develop sophisticated deep learning models. This lack of publicly accessible data also hinders the advancement of clinical science. Our digital pathology corpus is an effort to place a large amount of clinical pathology images collected at Temple University Hospital into the public domain to support the development of automatic interpretation technology. The goal of this ambitious project is to create a corpus of 1M images. We have already released 10,000 images from 600 clinical cases. In this paper, we describe the corpus under development and discuss some of the underlying technology that was developed to support this project.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10579-020-09503-7
