Parsing is the process of breaking a sentence into its grammatical components
and identifying the syntactic structure of the sentence. The syntactically
correct sentence structure is achieved by assigning grammatical labels to its
constituents using lexicon and syntactic rules. In linguistics, parser is
extremely useful due to the number of different applications like name entity
recognition, QA systems and information extraction, etc. The two most common
techniques used for parsing are phrase structure and dependency Structure.
Because Urdu is a low-resource language, there has been little progress in
building an Urdu parser. A comparison of several parsers revealed that the
dependency parsing approach is better suited for order-free languages such as
Urdu. We have made significant progress in parsing Urdu, a South Asian language
with a complex morphology. For Urdu dependency parsing, a basic feature model
consisting of word location, word head, and dependency relation is employed as
a starting point, followed by more complex feature models. The dependency
tagset is designed after careful consideration of the complex morphological
structure of the Urdu language, word order variation, and lexical ambiguity and
it contains 22 tags. Our dataset comprises of sentences from news articles, and
we tried to include sentences of different complexity (which is quite
challenging), to get reliable results. All experiments are performed using
MaltParser, exploring all 9 algorithms and classifiers. We have achieved a 70
percent overall best-labeled accuracy (LA), as well as an 84 percent overall
best-unlabeled attachment score (UAS) using the Nivreeager algorithm. The
comparison of output data with treebank test data that has been manually parsed
is then used to carry out error assessment and to identify the errors produced
by the parser.

通过依存句法分析技术，我们在低资源语言乌尔都语的解析中取得了重要进展，并使用复杂的特征模型和 Nivreeager 算法，获得了一定准确率并评估了解析器的错误。

探索乌尔都语中的句法模式：依存分析的深度研究

Exploring Syntactic Patterns in Urdu: A Deep Dive into Dependency  Analysis

Word Error Rate (WER) is the primary metric used to assess automatic speech
recognition (ASR) model quality. It has been shown that ASR models tend to have
much higher WER on speakers with speech impairments than typical English
speakers. It is hard to determine if models can be be useful at such high error
rates. This study investigates the use of BERTScore, an evaluation metric for
text generation, to provide a more informative measure of ASR model quality and
usefulness. Both BERTScore and WER were compared to prediction errors manually
annotated by Speech Language Pathologists for error type and assessment.
BERTScore was found to be more correlated with human assessment of error type
and assessment. BERTScore was specifically more robust to orthographic changes
(contraction and normalization errors) where meaning was preserved.
Furthermore, BERTScore was a better fit of error assessment than WER, as
measured using an ordinal logistic regression and the Akaike's Information
Criterion (AIC). Overall, our findings suggest that BERTScore can complement
WER when assessing ASR model performance from a practical perspective,
especially for accessibility applications where models are useful even at lower
accuracy than for typical speech.

该研究调查了使用 BERTScore 作为语音识别模型质量评估的度量标准。BERTScore 与 WER 相比，在计算错误类型和评估方面更符合人工评定的标准。因此，BERTScore 可以在实际应用中辅助 WER，特别是在无障碍应用中，即使模型的精度比典型语音的精度低，模型仍然是有用的。