NLP - Cube

Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging, Dependency Parsing and Named Entity Recognition for more than 50 languages

Named entity recognition with Graph-Based-Decoding

During the PARSEME Shared Task we experimented with a new architecture and algorithm for identifying expression. You can read the paper here.

We started working on integrating this technique into NLP-Cube and we are almost done. During our experiments we tested the unmodified GBG on Named Entity Recognition. We used the CONLL2003 corpus and got some pretty interesting results. According to the ACL Wiki, the best performing system is that of Huang et al. (2015) with a 90.1% F-Score. Our system achieved an F-Score of 95.12% for token-based evaluation (which is the same score used in the previously mentioned evaluation) and 93.40% strict F-Score (where we only counted NERs that are fully recognized and we discard partial matches).

Full evaluation output (using the PARSEME evaluation script):

## Global evaluation
* MWE-based: P=5369/5849=0.9179 R=5369/5648=0.9506 F=0.9340
* Tok-based: P=7884/8465=0.9314 R=7884/8112=0.9719 F=0.9512

## Per-category evaluation (partition of Global)
* LOC: MWE-proportion: gold=1668/5648=30% pred=1580/5849=27%
* LOC: MWE-based: P=1472/1580=0.9316 R=1472/1668=0.8825 F=0.9064
* LOC: Tok-based: P=1675/1797=0.9321 R=1675/1925=0.8701 F=0.9001
* MISC: MWE-proportion: gold=702/5648=12% pred=743/5849=13%
* MISC: MWE-based: P=567/743=0.7631 R=567/702=0.8077 F=0.7848
* MISC: Tok-based: P=743/998=0.7445 R=743/918=0.8094 F=0.7756
* ORG: MWE-proportion: gold=1661/5648=29% pred=1835/5849=31%
* ORG: MWE-based: P=1469/1835=0.8005 R=1469/1661=0.8844 F=0.8404
* ORG: Tok-based: P=2250/2738=0.8218 R=2250/2496=0.9014 F=0.8598
* PER: MWE-proportion: gold=1617/5648=29% pred=1691/5849=29%
* PER: MWE-based: P=1527/1691=0.9030 R=1527/1617=0.9443 F=0.9232
* PER: Tok-based: P=2677/2932=0.9130 R=2677/2773=0.9654 F=0.9385

## MWE continuity (partition of Global)
* Continuous: MWE-proportion: gold=5648/5648=100% pred=5755/5849=98%
* Continuous: MWE-based: P=5369/5755=0.9329 R=5369/5648=0.9506 F=0.9417
* Discontinuous: MWE-proportion: gold=0/5648=0% pred=94/5849=2%
* Discontinuous: MWE-based: P=0/94=0.0000 R=0/0=0.0000 F=0.0000

## Number of tokens (partition of Global)
* Multi-token: MWE-proportion: gold=2074/5648=37% pred=2209/5849=38%
* Multi-token: MWE-based: P=1941/2209=0.8787 R=1941/2074=0.9359 F=0.9064
* Single-token: MWE-proportion: gold=3574/5648=63% pred=3640/5849=62%
* Single-token: MWE-based: P=3428/3640=0.9418 R=3428/3574=0.9591 F=0.9504

## Whether seen in train (partition of Global)
* Seen-in-train: MWE-proportion: gold=3021/5648=53% pred=3011/5849=51%
* Seen-in-train: MWE-based: P=2941/3011=0.9768 R=2941/3021=0.9735 F=0.9751
* Unseen-in-train: MWE-proportion: gold=2627/5648=47% pred=2838/5849=49%
* Unseen-in-train: MWE-based: P=2428/2838=0.8555 R=2428/2627=0.9242 F=0.8886

## Whether identical to train (partition of Seen-in-train)
* Variant-of-train: MWE-proportion: gold=10/3021=0% pred=14/3011=0%
* Variant-of-train: MWE-based: P=7/14=0.5000 R=7/10=0.7000 F=0.5833
* Identical-to-train: MWE-proportion: gold=3011/3021=100% pred=2997/3011=100%
* Identical-to-train: MWE-based: P=2934/2997=0.9790 R=2934/3011=0.9744 F=0.9767