  Named entity recognition is one of the core tasks in NLP. Although many
improvements have been made on this task during the last years, the
state-of-the-art systems do not explicitly take into account the recursive
nature of language. Instead of only treating the text as a plain sequence of
words, we incorporate a linguistically-inspired way to recognize entities based
on syntax and tree structures. Our model exploits syntactic relationships among
words using a Tree-LSTM guided by dependency trees. Then, we enhance these
features by applying relative and global attention mechanisms. On the one hand,
the relative attention detects the most informative words in the sentence with
respect to the word being evaluated. On the other hand, the global attention
spots the most relevant words in the sequence. Lastly, we linearly project the
weighted vectors into the tagging space so that a conditional random field
classifier predicts the entity labels. Our findings show that the model detects
words that disclose the entity types based on their syntactic roles in a
sentence (e.g., verbs such as speak and write are attended when the entity type
is PERSON, whereas meet and travel strongly relate to LOCATION). We confirm our
findings and establish a new state of the art on two datasets.
