Dependency Parsing
321 papers with code • 15 benchmarks • 14 datasets
Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between "head" words and words, which modify those heads.
Example:
root
|
| +-------dobj---------+
| | |
nsubj | | +------det-----+ | +-----nmod------+
+--+ | | | | | | |
| | | | | +-nmod-+| | | +-case-+ |
+ | + | + + || + | + | |
I prefer the morning flight through Denver
Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents (+ indicates the dependent).
Libraries
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Latest papers
When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality
Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible.
Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing
The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018).
End-to-End Argument Mining over Varying Rhetorical Structures
Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures.
calamanCy: A Tagalog Natural Language Processing Toolkit
We introduce calamanCy, an open-source toolkit for constructing natural language processing (NLP) pipelines for Tagalog.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences.
To token or not to token: A Comparative Study of Text Representations for Cross-Lingual Transfer
To understand the downstream implications of text representation choices, we perform a comparative analysis on language models having diverse text representation modalities including 2 segmentation-based models (\texttt{BERT}, \texttt{mBERT}), 1 image-based model (\texttt{PIXEL}), and 1 character-level model (\texttt{CANINE}).
On the Challenges of Fully Incremental Neural Dependency Parsing
Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing.
High-order Joint Constituency and Dependency Parsing
Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of $O(n^4)$ time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction.
Assessment of Pre-Trained Models Across Languages and Grammars
We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures.
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy.