Pairwise FastText Classifier for Entity Disambiguation

Pairwise FastText Classifier for Entity Disambiguation Cheng Yua,b, Bing Chub, Rohit Ramb, James Aichingerb, Lizhen Qub,c, Hanna Suominenb,c a Project Cleopatra, Canberra, Australia b The Australian National University c DATA 61, Australia cheng@projectcleopatra.com.au {u5470909,u5568718,u5016706, Hanna.Suominen}@anu.edu.au Lizhen.Qu@data61.csiro.au Abstract For the Australasian Language Technology Association (ALTA) 2016 Shared Task, we devised Pairwise FastText Classifier (PFC), an efficient embedding-based text classifier, and used it for entity disambiguation. Com- pared with a few baseline algorithms, PFC achieved a higher F1 score at 0.72 (under the team name BCJR). To generalise the model, we also created a method to bootstrap the training set deterministically without human labelling and at no financial cost. By releasing PFC and the dataset augmentation software to the public1, we hope to invite more collabora- tion. 1 Introduction The goal of the ALTA 2016 Shared Task was to disambiguate two person or organisation entities (Chisholm et al., 2016). The real-world motiva- tion for the Task includes gathering information about potential clients, and law enforcement. We designed a Pairwise FastText Classifier (PFC) to disambiguate the entities (Chisholm et al., 2016). The major source of inspiration for PFC came from FastText 2 algorithm which achieved quick and accurate text classification (Joulin et al., 2016). We also devised a method to augment our training examples deterministically, and released all source code to the public. The rest of the paper will start with PFC and a mixture model based on PFC, and proceeds to pre- sent our solution to augment the labelled dataset 1 All source code can be downloaded from: https://github.com/projectcleopatra/PFC deterministically. Then we will evaluate PFC’s performance against a few baseline methods, in- cluding SVC3 with hand-crafted text features. Fi- nally, we will discuss ways to improve disambig- uation performance using PFC. 2 Pairwise Fast-Text Classifier (PFC) Our Pairwise FastText Classifier is inspired by the FastText. Thus this section starts with a brief description of FastText, and proceeds to demon- strate PFC. 2.1 FastText FastText maps each vocabulary to a real-valued vector, with unknown words having a special vo- cabulary ID. A document can be represented as the average of all these vectors. Then FastText will train a maximum entropy multi-class classi- fier on the vectors and the output labels. Fast Text has been shown to train quickly and achieve pre- diction performance comparable to Recurrent Neural Network embedding model for text classi- fication (Joulin et al., 2016). 2.2 PFC PFC is similar to FastText except that PFC takes two inputs in the form of a list of vocabulary IDs, because disambiguation requires two URL inputs. We specify that each of them is passed into the same embedding matrix. If each entity is repre- sented by a d dimensional vector, then we can concatenate them, and represent the two entities 2 The original paper of FastText used the typography fastText 3 SVC: Support vector classification Cheng Yu, Bing Chu, Rohit Ram, James Aichinger, Lizhen Qu and Hanna Suominen. 2016. Pairwise FastText Classifier for Entity Disambiguation. In Proceedings of Australasian Language Technology Association Workshop, …

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