Matthew R. Gormley

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.

Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction

Compositional embedding models build a representation for a linguistic structure based on its component word embeddings. While recent work has combined these word embeddings with hand crafted features for improved performance, it was restricted to a small number of features due to model complexity, thus limiting its applicability. We propose a new model that conjoins features and word embeddings while maintaing a small number of parameters by learning feature embeddings jointly with the parameters of a compositional model. The result is a method that can scale to more features and more labels, while avoiding overfitting. We demonstrate that our model attains state-of-the-art results on ACE and ERE fine-grained relation extraction.

Low-Resource Semantic Role Labeling

We explore the extent to which highresource manual annotations such as treebanks are necessary for the task of semantic role labeling (SRL). We examine how performance changes without syntactic supervision, comparing both joint and pipelined methods to induce latent syntax. This work highlights a new application of unsupervised grammar induction and demonstrates several approaches to SRL in the absence of supervised syntax. Our best models obtain competitive results in the high-resource setting and state-ofthe-art results in the low resource setting, reaching 72.48% F1 averaged across languages. We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure. 1

Embedding Lexical Features via Low-Rank Tensors

Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers of features, which can lead to over-fitting. We present a new model that represents complex lexical features---comprised of parts for words, contextual information and labels---in a tensor that captures conjunction information among these parts. We apply low-rank tensor approximations to the corresponding parameter tensors to reduce the parameter space and improve prediction speed. Furthermore, we investigate two methods for handling features that include

A Concrete Chinese NLP Pipeline

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-grams of mixed lengths. Our model achieves state-of-the-art results on tasks in relation extraction, PP-attachment, and preposition disambiguation.

Topic Models and Metadata for Visualizing Text Corpora

Natural language processing research increasingly relies on the output of a variety of syntactic and semantic analytics. Yet integrating output from multiple analytics into a single framework can be time consuming and slow research progress. We present a CONCRETE Chinese NLP Pipeline: an NLP stack built using a series of open source systems integrated based on the CONCRETE data schema. Our pipeline includes data ingest, word segmentation, part of speech tagging, parsing, named entity recognition, relation extraction and cross document coreference resolution. Additionally, we integrate a tool for visualizing these annotations as well as allowing for the manual annotation of new data. We release our pipeline to the research community to facilitate work on Chinese language tasks that require rich linguistic annotations.