Nicholas Andrews

Robust Entity Clustering via Phylogenetic Inference

Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline architecture that clusters the mentions using fixed or learned measures of name and context similarity. In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection process. We present a block Gibbs sampler for posterior inference and an empirical evaluation on several datasets.

Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation.

Work on cross document coreference resolution (CDCR) has primarily focused on news articles, with little to no work for social media. Yet social media may be particularly challenging since short messages provide little context, and informal names are pervasive. We introduce a new Twitter corpus that contains entity annotations for entity clusters that supports CDCR. Our corpus draws from Twitter data surrounding the 2013 Grammy music awards ceremony, providing a large set of annotated tweets focusing on a single event. To establish a baseline we evaluate two CDCR systems and consider the performance impact of each system component. Furthermore, we augment one system to include temporal information, which can be helpful when documents (such as tweets) arrive in a specific order. Finally, we include annotations linking the entities to a knowledge base to support entity linking. Our corpus is available: https: //bitbucket.org/mdredze/tgx 1 Entity Disambiguation Who is who and what is what? Answering such questions is usually the first step towards deeper semantic analysis of documents, e.g., extracting relations and roles between entities and events. Entity disambiguation identifies real world entities from textual references. Entity linking – or more generally Wikification (Ratinov et al., 2011) – disambiguates reference in the context of a knowledge base, such as Wikipedia (Cucerzan, 2007; McNamee and Dang, 2009; Dredze et al., 2010; Zhang et al., 2010; Han and Sun, 2011). Entity linking systems use the name mention and a context model to identify possible candidates and disambiguate similar entries. The context model includes a variety of information from the context, such as the surrounding text or facts extracted from the document. Though early work on the task goes back to Cucerzan (2007), the name entity linking was first introduced as part of TAC KBP 2009 (McNamee and Dang, 2009). Without a knowledge base, cross-document coreference resolution (CDCR) clusters mentions to form entities (Bagga and Baldwin, 1998b). Since 2011, CDCR has been included as a task in TAC-KBP (Ji et al., 2011) and has attracted renewed interest (Baron and Freedman, 2008b; Rao et al., 2010; Lee et al., 2012; Green et al., 2012; Andrews et al., 2014). Though traditionally a task restricted to small collections of formal documents (Bagga and Baldwin, 1998b; Baron and Freedman, 2008a), recent work has scaled up CDCR to large heterogenous corpora, e.g. the Web (Wick et al., 2012; Singh et al., 2011; Singh et al., 2012). While both tasks have traditionally considered formal texts, recent work has begun to consider informal genres, which pose a number of interesting challenges, such as increased spelling variation and (especially for Twitter) reduced context for disambiguation. Yet entity disambiguation, which links mentions across documents, is especially important for social media, where understanding an event often requires reading multiple short messages, as opposed to news articles, which have extensive background information. For example, there have now been several papers to consider named entity recognition in social media, a key first step in an entity disambiguation pipeline (Finin et al., 2010; Liu et al., 2011; Ritter et al., 2011; Fromreide et al., 2014; Li et al., 2012; Liu et al., 2012; Cherry and Guo, 2015; Peng and Dredze, 2015). Additionally, some have explored entity linking in Twitter (Liu et al., 2013; Meij et al., 2012; Guo et al., 2013), and have created datasets to support evaluation. However, to date no study has evaluated CDCR on social media data,1 and there is no annotated corpus to support such an effort. In this paper we present a new dataset that supports CDCR in Twitter: the TGX corpus (Twitter Grammy X-doc), a collection of Tweets collected around the 2013 Grammy music awards ceremony. The corpus includes tweets containing references to people, and references are annotated both for entity linking and CDCR. To explore this task for social media data and consider the challenges, opportunities and the performance of state of the art CDCR methods, we evaluate two state-of-the-art CDCR systems. Additionally, we modify one of these systems to incorporate temporal information associated with the corpus. Our results include improved performance for this task, and an analysis of challenges associated with CDCR in social media. 2 Corpus Construction A number of datasets have been developed to evaluate CDCR, and since the introduction of the TACKBP track in 2009, some now include links to a KB (e.g. Wikipedia). See Singh et al. (2012) for a detailed list of datasets. For Twitter, there have been several recent entity linking datasets, all of which number in the hundreds of tweets (Meij et al., 2012; Liu et al., 2013; Guo et al., 2013). None are annotated to support CDCR. Our goal is the creation of a Twitter corpus to support CDCR, which will be an order of magnitude larger than corresponding Twitter corpora for entity linking. We created a corpus around the 2013 Grammy Music Awards ceremony. The popular ceremony lasted several hours generating many Andrews et al. (2014) include CDCR results on an early version of our dataset but did not provide any dataset details or analysis. Additionally, their results averaged over many folds, whereas we will include results on the official dev/test splits. tweets. It included many famous people that are in Wikipedia, making it suitable for entity linking and aiding CDCR annotation. Additionally, Media personalities often have popular nicknames, creating an opportunity for name variation analysis. Using the Twitter streaming API2, we collected tweets during the event on Feb 10, 2013 between 8pm and 11:30pm Eastern time (01:00am and 04:30 GMT). We used Carmen geolocation3 (Dredze et al., 2013) to identify tweets that originated in the United States or Canada and removed tweets that were not identified as English according to the Twitter metadata. We then selected tweets containing “grammy” (case insensitive, and including “#grammy”), reducing 564,892 tweets to 50,429 tweets. Tweets were processed for POS and NER using Twitter NLP Tools 4 (Ritter et al., 2011). Tweets that did not include a person mention were removed. Using an automated NER system may miss some tweets, especially those with high variation in person names, but it provided a fast and effective way to identify tweets to include in our data set. For simplicity, we randomly selected a single person reference per tweet.5 The final set contained 15,736 tweets. We randomly selected 5,000 tweets for annotation, a reasonably sized subset for which we could ensure consistent annotation. Each tweet was examined by two annotators who grouped the mentions into clusters (CDCR) and identified the corresponding Wikipedia page for the entity if it existed (entity linking). As part of the annotation, annotators fixed incorrectly identified mention strings. Similar to Guo et al. (2013), ambiguous mentions were removed, but unlike their annotations, we kept all persons including those not in Wikipedia. Mentions that were comprised of usernames were excluded. The final corpus contains 4,577 annotated tweets, 10,736 unlabeled tweets, and 273 entities, of which 248 appear in Wikipedia. The corpus is divided into five folds by entity (about 55 entities per fold), 2 https://dev.twitter.com/streaming/reference/get/statuses/ sample 3 https://github.com/mdredze/carmen https://github.com/aritter/twitter_nlp In general, within document coreference is run before CDCR, and the cross-document task is to cluster withindocument coreference chains. In our case, there were very few mentions to the same person within the same tweet, so we did not attempt to make within-document coreference decisions. Mentions per entity: mean 16.77 Mentions per entity: median 1 Number of entities 273 Number of mentions (total tweets) 15,313 Number of unique mention strings 1,737 Number of singleton entities 166 Number of labeled tweets 4,577 Number of unlabeled tweets 10,736 Words/tweet (excluding name): mean 10.34 Words/tweet (excluding name): median 9 Table 1: Statistics describing the TGX corpus. where splits were obtained by first sorting the entities by number of mentions, then doing systematic sampling of the entities on the sorted list. The first split is reserved for train/dev purposes and the remaining splits are reserved for testing. This allows for a held out evaluation instead of relying on cross-validation, which ensures that future work can conduct system development without the use of the evaluation set. Some summary statistics appear in Table 1 and examples of entities in Table 2. The full corpus, including annotations (entity linking and CDCR), POS and NER tags are available at https://bitbucket.org/mdredze/tgx.6

Seeded Discovery of Base Relations in Large Corpora

Relationship discovery is the task of identifying salient relationships between named entities in text. We propose novel approaches for two sub-tasks of the problem: identifying the entities of interest, and partitioning and describing the relations based on their semantics. In particular, we show that term frequency patterns can be used effectively instead of supervised NER, and that the p-median clustering objective function naturally uncovers relation exemplars appropriate for describing the partitioning. Furthermore, we introduce a novel application of relationship discovery: the unsupervised identification of protein-protein interaction phrases.

Bayesian Modeling of Lexical Resources for Low-Resource Settings.

Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.

A Concrete Chinese NLP Pipeline

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.