Angli Liu, Stephen Soderland, Jonathan Bragg, Christopher H. Lin, Xiao Ling, Daniel S. Weld. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.

Multi-document summarization (MDS) systems have been designed for short, un-structured summaries of 10-15 documents, and are inadequate for larger document collections. We propose a new approach to scaling up summarization called hierar-chical summarization, and present the first implemented system, SUMMA. SUMMA produces a hierarchy of relatively short summaries, in which the top level provides a general overview and users can navigate the hierarchy to drill down for more details on topics of interest. SUMMA optimizes for coherence as well as cover-age of salient information. In an Amazon Mechanical Turk evaluation, users pref-ered SUMMA ten times as often as flat MDS and three times as often as timelines. 1

Distant supervision has become the lead-ing method for training large-scale rela-tion extractors, with nearly universal adop-tion in recent TAC knowledge-base pop-ulation competitions. However, there are still many questions about the best way to learn such extractors. In this paper we investigate four orthogonal improvements: integrating named entity linking (NEL) and coreference resolution into argument identification for training and extraction, enforcing type constraints of linked argu-ments, and partitioning the model by rela-tion type signature. We evaluate sentential extraction perfor-mance on two datasets: the popular set of NY Times articles partially annotated by Hoffmann et al. (2011) and a new dataset, called GORECO, that is comprehensively annotated for 48 common relations. We find that using NEL for argument identi-fication boosts performance over the tra-ditional approach (named entity recogni-tion with string match), and there is further improvement from using argument types. Our best system boosts precision by 44% and recall by 70%. 1

Chambers and Jurafsky (2009) demonstrated that event schemas can be automatically induced from text corpora.However, our analysis of their schemas identifies several weaknesses, e.g., some schemas lack a common topic and distinct roles are incorrectly mixed into a single actor.It is due in part to their pair-wise representation that treats subjectverb independently from verb-object.This often leads to subject-verb-object triples that are not meaningful in the real-world.We present a novel approach to inducing open-domain event schemas that overcomes these limitations.Our approach uses cooccurrence statistics of semantically typed relational triples, which we call Rel-grams (relational n-grams).In a human evaluation, our schemas outperform Chambers's schemas by wide margins on several evaluation criteria.Both Rel-grams and event schemas are freely available to the research community.

How do we scale information extraction to the massive size and unprecedented heterogeneity of the Web corpus? Beginning in 2003, our KnowItAll project has sought to extract high-quality knowledge from the Web. In 2007, we introduced the Open Information Extraction (Open IE) paradigm which eschews handlabeled training examples, and avoids domainspecific verbs and nouns, to develop unlexicalized, domain-independent extractors that scale to the Web corpus. Open IE systems have extracted billions of assertions as the basis for both commonsense knowledge and novel question-answering systems. This paper describes the second generation of Open IE systems, which rely on a novel model of how relations and their arguments are expressed in English sentences to double precision/recall compared with previous systems such as TEXTRUNNER and WOE. 1

Open Information Extraction extracts relations from text without requiring a pre-specified domain or vocabulary. While existing techniques have used only shallow syntactic features, we investigate the use of semantic role labeling techniques for the task of Open IE. Semantic role labeling (SRL) and Open IE, although developed mostly in isolation, are quite related. We compare SRL-based open extractors, which perform computationally expensive, deep syntactic analysis, with TextRunner, an open extractor, which uses shallow syntactic analysis but is able to analyze many more sentences in a fixed amount of time and thus exploit corpus-level statistics. Our evaluation answers questions regarding these systems, including, can SRL extractors, which are trained on PropBank, cope with heterogeneous text found on the Web? Which extractor attains better precision, recall, f-measure, or running time? How does extractor performance vary for binary, n-ary and nested relations? How much do we gain by running multiple extractors? How do we select the optimal extractor given amount of data, available time, types of extractions desired?

The bare minimum lexical resource required to translate between a pair of languages is a translation dictionary. Unfortunately, dictionaries exist only between a tiny fraction of the 49 million possible language-pairs making machine translation virtually impossible between most of the languages. This paper summarizes the last four years of our research motivated by the vision of panlingual communication. Our research comprises three key steps. First, we compile over 630 freely available dictionaries over the Web and convert this data into a single representation – the translation graph. Second, we build several inference algorithms that infer translations between word pairs even when no dictionary lists them as translations. Finally, we run our inference procedure offline to construct PANDICTIONARY– a sense-distinguished, massively multilingual dictionary that has translations in more than 1000 languages. Our experiments assess the quality of this dictionary and find that we have 4 times as many translations at a high precision of 0.9 compared to the English Wiktionary, which is the lexical resource closest to PANDICTIONARY.

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.

Text classification poses a significant challenge for knowledge-based technologies because it touches on all the familiar demons of artificial intelligence: the knowledge engineering bottleneck, problems of scale, easy portability across multiple applications, and cost-effective system construction. Information retrieval (IR) technologies traditionally avoid all of these issues by defining a document in terms of a statistical profile of its lexical items. The IR community is willing to exploit a superficial type of knowledge found in dictionaries and thesaurae, but anything that requires customization, application-specific engineering, or any amount of manual tinkering is thought to be incompatible with practical cost-effective system designs. In this paper those assumptions are challenged and it is shown how machine learning techniques can operate as an effective method for automated knowledge acquisition when it is applied to a representative training corpus, and leveraged against a few hours of routine work by a domain expert. A fully implemented text classification system operating on a medical testbed is described and experimental results based on that testbed are reported.

The vast amounts of on-line text now available have ledto renewed interest in information extraction (IE) systems thatanalyze unrestricted text, producing a structured representation ofselected information from the text. This paper presents a novel approachthat uses machine learning to acquire knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE discourse component that makes intersentential inferences and identifies logicalrelations among information extracted from the text. Previous corpus-based approaches were limited to lower level processing such as part-of-speech tagging, lexical disambiguation, and dictionary construction. Wrap-Up is fully trainable, and not onlyautomatically decides what classifiers are needed, but even derives the featureset for each classifier automatically. Performance equals that of a partially trainable discourse module requiring manual customization for each domain.