Reproducing Chen & Manning (2014)

Neural dependency parsing is attractive for several reasons: first, distributed representation generalizes better, second, fast parsing unlocks new applications, and third, fast training means parsers can be co-trained with other NLP modules and integrated into a bigger system.

Chen & Manning (2014) from Stanford were the first to show that neural dependency parsing works and Google folks were quick to adopt this paradigm to improve the state-of-the-art (e.g. Weiss et al., 2015).

Though Stanford open-sourced their parser as part of CoreNLP, they didn’t release the code of their experiments. As anybody in academia probably knows, reproducing experiments is non-trivial, even extremely difficult at times. Since I have painstakingly gone through the process, I think it’s a good idea to share with you.

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Knowledge base completion 101

Knowledge base completion (KBC) is not a standard task in natural language processing nor in machine learning. A search on Google scholar results in only over 100 article containing this phrase. Although it is similar to link prediction, “a long-standing challenge in modern information science” (Lü & Zhou, 2011), it has received much less attention.

However KBC is potentially an important step towards natural language understanding and recent advances in representation learning have enabled researchers to learn larger datasets with improved precision. Actually, a half of KBC articles were published in or after 2010. Continue reading