Open Information Extraction systems, starting with TextRunner Yates et al. (2007), seek to extract all relational tuples expressed in text, without being bound to an anticipated list of predicates. Such systems have been used recently for relation extraction Soderland et al. (2013), question-answering Fader et al. (2014), and for building domain-targeted knowledge bases Mishra et al. (2017), among others.
Subsequent extractors (ReVerb Fader et al. (2011), Ollie Mausam et al. (2012), ClausIE Del Corro and Gemulla (2013), Stanford Open IE Angeli et al. (2015), OpenIE4 Mausam (2016), MinIE Gashteovski et al. (2017)) have sought to improve yield and precision, i.e. the number of facts extracted from a given corpus, and the proportion of those facts that is deemed correct.
Nonetheless, the task definition is underspecified, and, to the best of our knowledge, there is no gold standard. Most evaluations require somewhat subjective and inconsistent judgment calls to be made about extracted tuples being acceptable or not. The most recent automatic benchmark of Stanovsky and Dagan (2016) has some shortcomings that we propose to tackle here, regarding the theory underlining the task definition as well as the evaluation procedure.
We manually performed the task of Open Information Extraction on 5 short documents, elaborating tentative guidelines, and resulting in a ground truth reference of 347 tuples. We evaluate against our benchmark the available OIE engines up to MinIE, with a fine-grained token-level evaluation. We distribute our resource and annotation guidelines, along with the evaluation script.111https://sites.google.com/view/wire57
2 Related Work
For their evaluation, typically, developers of Open IE systems pool the output of various systems on a given corpus. They label a sample of produced tuples as correct or incorrect, with the general guideline that an extraction is correct if it is implied by the sentence. Thus, ollie-emnlp12 write: “Two annotators tagged the extractions as correct if the sentence asserted or implied that the relation was true.” DelCorro:2013:CCO:2488388.2488420 propose: “We also asked labelers to be liberal with respect to coreference or entity resolution; e.g., a proposition such as (‘he’ ; ‘has’ ; ‘office’), or any unlemmatized version thereof, is treated as correct.” DBLP:conf/acl/SahaPM17: “We sample a random testset of 2,000 sentences […] Two annotators with NLP experience annotate each extraction for correctness.” Gashteovski et al. (2017): “A triple is labeled as correct if it is entailed by its corresponding clause.” Then, precision and yield are used as performance metrics. Without a reference, recall is naturally impossible to measure.
We define a reference a priori. This allows for automatic scoring of systems’ outputs, which greatly diminishes subjectivity from the process of labelling facts “for correctness”. Above all, it is meant to help researchers agree on what the task precisely entails. As a consequence, it allows to measure a true recall (albeit on a small corpus).
The complexity of our guidelines is indicative of all that is swept under the carpet when “annotating for correctness”. As a matter of fact, when closely examining other references for OIE, many extracted tuples eventually labelled as “good” have more or less important issues. Some really dubious cases are hard to gauge and their labelling is ultimately subjective. To showcase the devilishly difficult judgment calls that this implies, compare the following two extractions. “‘The opportunity is significant and I hope we can take the opportunity to move forward,’ he said referring to his coming trip to Britain.” yields (his ; has ; coming trip), and “[…], the companies included CNN, but not its parent, AOL Time Warner” yields (its ; has ; parent). Are the extractions implied by the sentence ? In Del Corro and Gemulla (2013), the annotator approved the latter, and rejected the former. The extraction (he ; said ; The opportunity is significant referring to his coming trip to Britain) was also deemed correct, despite the composed second argument.
Some other tasks for which OIE output is used, such as Open QA Fader et al. (2014), TAC-KBP Soderland et al. (2013), or textual similarity and reading comprehension as in Stanovsky et al. (2015) — could in principle be used to compare extractors’ performance, but only give a very coarse-grained signal, mostly unaffected by the tuning of systems.
A promising method is that explored by Mishra et al. (2017) for the Aristo KB.222http://data.allenai.org/tuple-kb/ Aristo is a science-focused KB extracted from a high-quality 7M-sentence corpus. The authors preprocessed a smaller, similarly science-related, independent corpus of 1.2M sentences, into a "Reference KB" of 4147 facts, validated by Turkers. Assuming that these 4147 facts are representative of the science domain as a whole, they measured comprehensiveness (recall) over this domain by measuring coverage on the Reference KB.
2.1 ORE benchmark
Mesquita et al. (2013) compare more or less deep ‘parsers’, including the OIE systems Ollie and ReVerb, on the germane task of Open Relation Extraction (ORE), between named entities. They build a benchmark of 662 binary relations over 1100 sentences from 3 sources (the Web, the New York Times and the Penn Treebank). They label an additional 222 NYT sentences with -ary relations (one per sentence), and 12,000 with automatic annotations.
Besides the named entity arguments, their annotations consist of one mandatory trigger word (indicating the relation), surrounded by a window of allowed tokens. To compare OIE with ORE systems, they have to replace the target entities by salient arguments (Asia and Europe) which are easy to recognize. They discuss some of the challenges that arise from divergent annotation styles and evaluation methods.
While the tasks are similar, restraining arguments to be Named Entities limits IE to capturing only the most salient relations expressed in the text. Allowing for any NP to be an argument, we extract 6 facts per sentence on average in the benchmark presented here, compared to 0.6 in the ORE dataset. We also annotate some relations that do not have a trigger in the sentence (such as (Paris ; [is in] ; France) from Chilly Gonzales lived in Paris, France).
2.2 QA-SRL OIE benchmark
Stanovsky2016EMNLP build a large benchmark for OIE, by automatically processing the QA-SRL dataset He et al. (2015). Precisely, for each predicate annotated in QA-SRL, they generate one tuple expressing each element of the Cartesian product of answers (excluding pronouns) to the questions about this predicate.
For instance, QA-SRL lists five questions asked about the sentence “Investors are appealing to the SEC not to limit their access to information about stock purchases and sales by corporate insiders” : “who are appealing to something ?”, “who are someone appealing to ?”, “what are someone appealing ?”, “what might not limit something ?” and “what might not someone limit ?”, with one answer per question. This generates the reference tuples (Investors ; appealing ; not to limit their access to information about stock purchases and sales by corporate insiders ; to the SEC) and (the SEC ; might not limit ; their access to information about stock purchases and sales by corporate insiders).
Their dataset is comprised of 10,359 tuples over 3200 sentences (from the Wall Street Journal and Wikipedia), and is available for download.333http://u.cs.biu.ac.il/ñlp/resources/downloads/
While this work makes a big step in the right direction, there are a few important issues with this benchmark.
First, a major strength of the dataset is its intended and partly achieved completeness, but we do not find it to be a suitably comprehensive reference against which to measure systems’ recall. This might be because the QA-SRL dataset doesn’t lend itself well to exhaustiveness in the realm of Open IE, partly because it is restricted to explicit predicates. For instance, the sentence “However, Paul Johanson, Monsanto’s director of plant sciences, said the company’s chemical spray overcomes these problems and is ‘gentle on the female organ’.” contains two predicates, generating the extractions (Paul Johanson ; said ; the company’s chemical spray overcomes these problems and is “gentle on the female organ.”) and (the company’s chemical spray ; overcomes ; these problems). Yet, that omits the (in our view useful) extractions (the company’s chemical spray ; is ; “gentle on the female organ”), and (Paul Johanson ; is ; Monsanto’s director of plant sciences).
Another issue is that some words not found in the original sentence were added by the SRL-to-QA process and retained in the QA-to-OIE transformation, and become part of the reference. In the example above, it is unclear how the second predicate “might not limit” is extracted from the sentence. Further, although in this particular case adding the modal is a good way of expressing the information, its repeated use by QA-SRL to produce questions waters down the expressed facts in the end. For instance, the uninformative triple (a manufacturer ; might get ; something) is generated from the sentence “…and if a manufacturer is clearly trying to get something out of it …”, with the same added “might”.
Last, the scoring procedure isn’t robust. Using the code made available by the authors444https://github.com/gabrielStanovsky/oie-benchmark — the scoring function was updated since its description in Stanovsky and Dagan (2016). We believe the function from the article suffers from similar issues., we were able to get top results with a dummy extractor.
This is because the scorer doesn’t penalize extractions for being too long, nor for misplacing parts of the relation in the object slot or vice versa. Therefore, if is an input sentence, a trivial system that "extracts" , , etc., will be given an unfairly great score. We implemented that program (dubbed Munchkin) which predictably performed well above other genuine extraction systems, as pictured in Figure 1.
2.3 RelVis benchmarking toolkit
Schneider et al. (2017) evaluate four systems (ClausIE, OpenIE 4, Stanford Open IE and PredPatt) against the two datasets mentioned above.555Their code is announced but not available as of this writing — https://github.com/schmaR/relvis They use two methods to match predicted and reference tuples : “containment” and “relaxed containment”. These methods mean that the predicted tuple must include the reference tuple, and that inclusion must happen for each argument, in the non-relaxed case. In the relaxed case the boundaries between parts of a tuple are ignored. Similarly to that of Stanovsky and Dagan (2016), this scoring procedure doesn’t penalize systems for returning overlong spans.
To compare facts with a reference, most authors require matching tuples to have the same number of arguments and to share the grammatical head words of each part (e.g. in Angeli and Manning (2013) and in the article of Stanovsky and Dagan (2016)). In the updated GitHub repository of Stanovsky and Dagan (2016), lexical match is used instead : more than half of the words of a predicted tuple must match the reference for it to be considered correct.
In contrast with these works and Schneider et al. (2017), our scorer penalizes verbosity by measuring precision at the token level. We penalize the omission of parts of a reference tuple by gradually diminishing recall (at the token level), instead of a sharp all-or-nothing criterion.
Mesquita et al. (2013) annotate relations as one mandatory target plus some optional complementary words, and treat arguments (named entities) in an ad-hoc fashion for OIE systems.
Open IE bears some similarity to the task of Semantic Role Labelling, as explored in Christensen et al. (2011), Mesquita et al. (2013), and as demonstrated by SRLIE, a component of the OpenIE 4 system.
In effect extracted tuples are akin to simplified PropBank666https://propbank.github.io — Kingsbury and Palmer (2002) or FrameNet Ruppenhofer et al. (2005) frames, and our annotations were inspired by those projects. Still, with a focus on extracting new relations at scale, optional arguments such as Propbank’s modifiers (ArgM) are discouraged in OIE. Another major difference is the vocabulary of predicates being open to any relational phrase, rather than belonging to a closed curated list such as VerbNet. Within reason, OIE seeks to extract rich and precise relations phrases.
3.1 Annotation process
A small corpora of 57 sentences taken from the beginning of 5 documents in English were used as the source text from which to extract tuples. Three documents are Wikipedia articles (Chilly Gonzales, the EM algorithm, and Tokyo) and two are newswire articles (taken from Reuters, hence the Wi-Re name).
Two annotators (authors of this paper) first independently extracted tuples from the documents, based on a first version of the annotation guidelines which quickly proved insufficient to reach any significant agreement. The two sets of annotations were then merged, and the guidelines rectified along the way in order to resolve the issues that arose. After merging, a quick test on a few additional sentences from a different document showed a much improved agreement. The guidelines are detailed in the next sections, more than half of extractions matching exactly and the remaining missing a few details.
3.2 Annotation principles
In keeping with past literature, our guiding principles for the annotation were as follows.
The first, obvious purpose of extracted information is to be informative. Fader et al. (2011) mention how extracting (Faust ; made ; a deal) instead of the correct (Faust ; made a deal with ; the devil) would be pointless. Further, anaphoric mentions being so ubiquitous and being void of meaning outside the context of their original sentence, we resolve anaphora in our extractions.
Moreover and following Stanovsky and Dagan (2016), extracted tuples should each be minimal, in the sense that they should convey the smallest standalone piece of information, though that piece must be completely expressed. This means that some facts must be extracted as -ary relations.777Some systems — namely CSD-IE Bast and Haussmann (2013) and NestIE Bhutani et al. (2016) — explore nesting extractions, but we didn’t adopt this strategy. The MinIE system Gashteovski et al. (2017) in particular addresses this issue and “minimizes its extractions by identifying and removing parts that are considered overly specific”.
The annotation shall be exhaustive
, in the sense of capturing as much of the information expressed in the text as possible. This is to measure absolute recall for a system, a notoriously difficult evaluation metric for Open IE.
This in turn raises the issue of inference : Some information is merely suggested by the text, rather than explicitly expressed, and should not be annotated. Light inference, in the form of reformulation, is helpful to make use of the information extracted, but full-fledged inference should be processed by a dedicated program, and is not part of the Open IE task.
Past authors mention this issue : from Wu and Weld (2010) : “The extractor should produce one triple for every relation stated explicitly in the text, but is not required to infer implicit facts.” Stanovsky and Dagan (2016) say: “an Open IE extractor should produce the tuple (John; managed to open; the door) but is not required to produce the extraction (John; opened; the door)”. In this resource we do also annotate (John; [opened]; the door), marking the reworded relation as inferred (which in turn makes it optional to find when scoring).
3.3 Annotation guidelines
Our full annotation guidelines are shared at https://sites.google.com/view/wire57. We present its major points here.
Extracted tuples should reflect all meaningful relationships found in the source text. Typically, this means that there are multiple tuples for a given sentence. A number of times, two arguments are connected in a sentence but the relation that links them is implicit (e.g. Paris, France, the North Atlantic Treaty Organization (NATO), the Nature paper or the Turing paper, etc.). In this case, we annotate a somewhat arbitrary relationship (such as is in, stands for, published in and published by respectively), the tokens of which are thus inferred. This is the case for 39% of our tuples.
Some OIE systems similarly attempt to hallucinate some or part of relations. Notably, ClausIE wrongly extracts (New Delhi ; is ; India), and MinIE gets right (Paris ; is in ; France). Ollie adds some “be” auxiliaries to otherwise nominal relations, as in Barack Obama, former president of the United States, […], which OpenIE 4 also infers. Yet, we acknowledge that most work in Open IE rely on explicit predicate tokens as in Mesquita et al. (2013), and don’t try to elicit relations further. At scoring time, systems are not penalized for not finding inferred words, or not finding inferred relations. If the whole predicate of a tuple is inferred, a predicted tuple is scored on its token overlap with the arguments only.
We suggest “platinum” annotations, including inferred words, to be a very high standard for extractors, while the gold standard for the task, recall-wise, is based only on sentence words.
Noun phrases can be rich in elements of information. To solve the problem of finding the granularity level to use when including argument NPs, we extract two tuples, one as generic as possible and the other as specific as possible, for the same relation. Adjectives and other elements of meaning that can be easily separated from the noun phrase to create other tuples are so split. Only elements that cannot become part of the most specific noun phrase.
For instance, the sentence “Solo Piano is a great album of classical piano compositions” would yield 3 tuples : the split adjective (Solo Piano ; is ; great), the generic (Solo Piano ; is ; [an] album) and the specific (Solo Piano ; is ; [an] album of classical piano compositions).
When predicates contain nouns or other elements (e.g. Tokyo is the capital of Japan.), we annotate the richer relationship (Tokyo ; is the capital of ; Japan) rather than the more basic (Tokyo ; is ; the capital of Japan). This allows tuple relations to be more meaningful, and more easily compared, clustered, and aggregated with other relations. This also is in line with ReVerb.
Like ClausIE and other extractors since, we split conjunctions : “Andrea lived in both Poland and Italy” yields both (Andrea ; lived in ; Poland) and (Andrea ; lived in ; Italy).
|Contains inferred words||186||54|
|-ary, = 3||72||21|
|-ary, = 4||16||5|
|-ary, = 5||1||0.3|
A sample of annotations is pictured in Figure 3. The occurring frequency of various phenomena is presented in Table 1. Our resource is comprised of 343 relational facts (or tuples), three quarters of them binary relations. One in five have three arguments, sometimes “two objects” as in (This performance ; has made ; some economists ; optimistic) or more frequently a complement as in (His parents ; had to flee ; from Hungary ; during World War II). Five percent of them have four arguments or more : for instance (Tokyo ; ranked ; third ; in the International Financial Centres Development IndexEdit ; twice) and (The International Monetary Fund ; saw ; advanced economies ; advancing 1.8 percent ; [in] 2017).
We found (and resolved) anaphoric phrases in more than half the tuples, as in (Emperor Meiji ; moved ; his/(Emperor Meiji’s) seat ; to (the city)/Tokyo ; from the old capital of Kyoto ; in 1868). The released dataset contains the raw and anaphora-resolved argument spans, as pictured in Figure 2.
When solely extracting words from the sentence would not yield clear factual tuples, we reworded or adapted the text into more explicit statements. In this case, we marked the changed (or added) words as inferred (brackets in Figure 3). For instance in sentence CE 4, the relation “[advanced]” was reformulated from the sentence word “advancing”, and the word [in] was added before “2017”. In the resource, each token is accompanied by its index in the sentence if it comes from it, or the “inferred” mark, as showed in Figure 2. Inferred words represent 13% of the lot but affect 54% of the tuples.
4 Evaluation of Existing Systems
An important step when measuring extractors performance is the scoring process. Matching a system’s output to a reference isn’t trivial. As detailed in Section 2.2, because it didn’t penalize overlong extractions, we could game the basic evaluation method of the QA-SRL OIE benchmark with a trivial extractor.
Our scorer computes precision and recall of a system’s predicted tuples at the token level. Precision is, briefly put, the proportion of extracted words that are found in the reference. Recall is the proportion of reference words found in the systems’ predictions.
More formally, let be the gold tuples, and a system’s extractions. We denote the parts of a tuple , where is the first argument, is the relation, etc., up to the fifth argument when it exists (no reference tuple contains more than 5 arguments). Let be the subset of words shared by parts and , where parts are considered as bags of words. The length of a tuple is the sum of lengths of its parts, i.e. .
A predicted tuple may match a reference tuple from the same sentence if it shares at least one word from each of the relation, first and second arguments with it. That is, iff exist such that and .
For all tuple pairs that may match, we have the matching scores:
We match predicted tuples with reference ones by greedily removing from the potential match pool the pair with maximum score, until no remaining tuples match. Let be the matching function such that matches with (and conversely matches ), assuming that if there is no match for .
Hence, the overall performance metrics of an extractor are its token-weighted precision and recall over all tuples, i.e.
To avoid penalizing systems for not finding them, neither the words annotated as inferred, nor the coreference information are used in this evaluation ( is the non-resolved version of the tuple, and inferred words are not included in recall denominators). Future work can look into evaluating OIE systems that mean to resolve anaphoric mentions.
In order to experiment with the 7 systems described in this paper, we installed them as a single web service on one computer. A client application need only submit a sentence and a list of OIE system names to perform extraction. All tuples are in turn served as JSON objects following a unique, documented pattern, no matter the OIE system used. This considerably facilitates the development of clients, shielded from the different tuple formats, various coding languages (Scala, Java, Python), and other quirks of the OIE systems presented here. It also allowed us to visualize the tuples using a web application, pictured in Figure 4. Moreover, because the various extractors run as servers, they load their respective resources (1 or 2 minutes) only once, when the service is launched, and are then always quick to respond to a given extraction task (a few seconds). Otherwise, the user would have had to wait for the resources to load each time when querying the extractors.
While creating such a framework is a significant effort, it ultimately saved us a lot of time when writing the clients. It also provided a common frame of reference for all collaborators in our lab. Typically, we used the default configuration for each OIE system, but we tweaked the available flags in order to favor exhaustiveness, when such flags were present and properly documented. When additional information did not fit into a traditional tuple (arg1; rel; arg2), e.g. MinIE’s quantities, we resorted to simple schemes to faithfully cast that information into a tuple.
Table 2 details the performance of available OIE systems against our reference. MinIE produces a large number of correct tuples, and performs best, especially recall-wise. The conservative choices made by ReVerb achieve a relatively high precision, though it lacks in comprehensiveness. Ollie improves recall over ReVerb, and Open IE 4 improves precision over Ollie. Stanford Open IE produces a very large number of tuples, hindering its precision (it is possible that limiting its verboseness through configuration would improve this).
We provide a tentative reference for the task of Open Information Extraction, consisting of 343 facts, including some implicit relations, over 57 sentences. A quarter of them are -ary relations and coreference information is included in over half of them. We distribute our annotation guidelines and their rationale along with our annotated resource. The thorniest issues are the fine line between useful reformulation of information to a canonical form and ill-advised inference, and how to trim and annotate complex noun-phrase arguments.
We assess the ReVerb, Ollie, ClausIE, Stanford Open IE, OpenIE 4, and MinIE systems against our reference, using a fine-grained token-level scorer, which we also release. We find the MinIE system Gashteovski et al. (2017) to perform best.
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