| { |
| "paper_id": "P12-1012", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:27:32.894072Z" |
| }, |
| "title": "Temporally Anchored Relation Extraction", |
| "authors": [ |
| { |
| "first": "Guillermo", |
| "middle": [], |
| "last": "Garrido", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "NLP & IR Group at UNED", |
| "institution": "", |
| "location": { |
| "settlement": "Madrid", |
| "country": "Spain" |
| } |
| }, |
| "email": "ggarrido@lsi.uned.es" |
| }, |
| { |
| "first": "Anselmo", |
| "middle": [], |
| "last": "Pe\u00f1as", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "NLP & IR Group at UNED", |
| "institution": "", |
| "location": { |
| "settlement": "Madrid", |
| "country": "Spain" |
| } |
| }, |
| "email": "anselmo@lsi.uned.es" |
| }, |
| { |
| "first": "Bernardo", |
| "middle": [], |
| "last": "Cabaleiro", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "NLP & IR Group at UNED", |
| "institution": "", |
| "location": { |
| "settlement": "Madrid", |
| "country": "Spain" |
| } |
| }, |
| "email": "bcabaleiro@lsi.uned.es" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graphbased document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.", |
| "pdf_parse": { |
| "paper_id": "P12-1012", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graphbased document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "A question that arises when extracting a relation is how to capture its temporal validity: Can we assign a period of time when the obtained relation held? As pointed out in (Ling and Weld, 2010) , while much research in automatic relation extraction has focused on distilling static facts from text, many of the target relations are in fact fluents, dynamic relations whose truth value is dependent on time (Russell and Norvig, 2010).", |
| "cite_spans": [ |
| { |
| "start": 173, |
| "end": 194, |
| "text": "(Ling and Weld, 2010)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "The Temporally anchored relation extraction problem consists in, given a natural language text document corpus, C, a target entity, e, and a target relation, r, extracting from the corpus the value of that relation for the entity, and a temporal interval for which the relation was valid.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "In this paper, we introduce a methodological approach to temporal anchoring of relations automatically extracted from unrestricted text. Our system (see Figure 1 ) extracts relational facts from text using distant supervision (Mintz et al., 2009) and then anchors the relation to an interval of temporal validity. The intuition is that a distant supervised system can effectively extract relations from the source text collection, and a straightforward date aggregation can then be applied to anchor them. We propose a four step process for temporal anchoring:", |
| "cite_spans": [ |
| { |
| "start": 226, |
| "end": 246, |
| "text": "(Mintz et al., 2009)", |
| "ref_id": "BIBREF14" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 153, |
| "end": 161, |
| "text": "Figure 1", |
| "ref_id": "FIGREF1" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "(1) represent temporal evidence; (2) select temporal information relevant to the relation; (3) decide how a relational fact and its relevant temporal information are themselves related; and (4) aggregate imprecise temporal intervals across multiple documents. In contrast with previous approaches that aim at intra-document temporal information extraction (Ling and Weld, 2010) , we focus on mining a corpus aggregating temporal evidences across the supporting documents.", |
| "cite_spans": [ |
| { |
| "start": 356, |
| "end": 377, |
| "text": "(Ling and Weld, 2010)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "We address the following research questions:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "(1) Validate whether distant supervised learning is suitable for the task, and evaluate its shortcomings.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "(2) Explore whether the use of features extracted from a document-level rich representation could improve distant supervised learning. (3) Compare the use of document metadata against temporal expressions within the document for relation temporal anchoring. (4) Analyze how, in a pipeline architecture, the propagation of errors limits the overall system's (2) Document Representation performance. The representation we use for temporal information is detailed in section 2; the rich document-level representation we exploit is described in section 3. For a query entity and target relation, the system first performs relation extraction (section 4); then, we find and aggregate time constraint evidence for the same relation across different documents, to establish a temporal validity anchor interval (section 5). Empirical comparative evaluation of our approach is introduced in section 6; while some related work is shown in section 7 and conclusions in section 8.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "We will denominate relation instance a triple entity, relation name, value . We aim at anchoring relation instances to their temporal validity. We need a representation flexible enough to capture the imprecise temporal information available in text, but expressed in a structured style. Allen's (1983) interval-based algebra for temporal representation and reasoning, underlies much research, such as the Tempeval challenges (Verhagen et al., 2007; Pustejovsky and Verhagen, 2009) . Our task is different, as we focus on obtaining the temporal interval associated to a fact, rather than reasoning about the temporal relations among the events appearing in a single text.", |
| "cite_spans": [ |
| { |
| "start": 287, |
| "end": 301, |
| "text": "Allen's (1983)", |
| "ref_id": "BIBREF1" |
| }, |
| { |
| "start": 425, |
| "end": 448, |
| "text": "(Verhagen et al., 2007;", |
| "ref_id": "BIBREF26" |
| }, |
| { |
| "start": 449, |
| "end": 480, |
| "text": "Pustejovsky and Verhagen, 2009)", |
| "ref_id": "BIBREF17" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchors", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Let us assume that each relation instance is valid during a certain temporal interval, I = [t 0 , t f ]. This sharp temporal interval fails to capture the imprecision of temporal boundaries conveyed in natural language text. The Temporal Slot Filling task at TAC-KBP 2011 (Ji et al., 2011) proposed a 4-tuple representation that we will refer to as imprecise anchor intervals. An imprecise temporal interval is defined as an ordered 4-tuple of time points: (t 1 , t 2 , t 3 , t 4 ), with the following semantics: the relation is true for a period which starts at some point between t 1 and t 2 and ends between t 3 and t 4 . It should hold that: t 1 \u2264 t 2 , t 3 \u2264 t 4 , and t 1 \u2264 t 4 . Any of the four endpoints can be left unconstrained (t 1 or t 3 would be \u2212\u221e, and t 2 or t 4 would be +\u221e). This representation is flexible and expressive, although it cannot capture certain types of information (Ji et al., 2011) .", |
| "cite_spans": [ |
| { |
| "start": 272, |
| "end": 289, |
| "text": "(Ji et al., 2011)", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 896, |
| "end": 913, |
| "text": "(Ji et al., 2011)", |
| "ref_id": "BIBREF8" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchors", |
| "sec_num": "2" |
| }, |
| { |
| "text": "We use a rich document representation that employs a graph structure obtained by augmenting the syntactic dependency analysis of the document with semantic information.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "A document D is represented as a document graph G D ; with node set V D and edge set, E D . Each node v \u2208 V D represents a chunk of text, which is a sequence of words 1 . Each node is labeled with a dictionary of attributes, some of which are common for every node: the words it contains, their part-ofspeech annotations (POS) and lemmas. Also, a representative descriptor, which is a normalized string value, is generated from the chunks in the node. Certain nodes are also annotated with one or more types. There are three families of types: Events (verbs that describe an action, annotated with tense, polarity and aspect); standardized Time Expressions; and Named Entities, with additional annotations such as gender or age.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Edges in the document graph, e \u2208 E D , represent four kinds of relations between the nodes:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 Syntactic: a dependency relation. \u2022 Temporal relations between events and time expressions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The processing includes dependency parsing, named entity recognition and coreference resolution, done with the Stanford CoreNLP software (Klein and Manning, 2003) ; and events and temporal information extraction, via the TARSQI Toolkit (Verhagen et al., 2005) .", |
| "cite_spans": [ |
| { |
| "start": 137, |
| "end": 162, |
| "text": "(Klein and Manning, 2003)", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 236, |
| "end": 259, |
| "text": "(Verhagen et al., 2005)", |
| "ref_id": "BIBREF25" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The document graph G D is then further transformed into a collapsed document graph, G C . Each node of G C clusters together coreferent nodes, representing a discourse referent. Thus, a node u in G C is a cluster of nodes u 1 , . . . , u k of G D . There is an edge (u, v) in G C if there was an edge between any of the nodes clustered into u and any of the nodes v 1 , . . . , v k . The coreference edges do not appear in this representation. Additional semantic information is also blended into this representation: normalization of genitives, semantic class indicators inferred from appositions and genitives, and gender annotation inferred from pronouns. A final graph example can be seen in Figure 2 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 696, |
| "end": 704, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Document Representation", |
| "sec_num": "3" |
| }, |
| { |
| "text": "To perform relation extraction, our proposal follows a distant supervision approach (Mintz et al., 2009) , which has also inspired other slot filling systems (Agirre et al., 2009; Surdeanu et al., 2010) . We capture long distance relations by introducing a document-level representation and deriving novel features from deep syntactic and semantic analysis.", |
| "cite_spans": [ |
| { |
| "start": 84, |
| "end": 104, |
| "text": "(Mintz et al., 2009)", |
| "ref_id": "BIBREF14" |
| }, |
| { |
| "start": 158, |
| "end": 179, |
| "text": "(Agirre et al., 2009;", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 180, |
| "end": 202, |
| "text": "Surdeanu et al., 2010)", |
| "ref_id": "BIBREF22" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Seed harvesting. From a reference Knowledge Base (KB), we extract a set of relation triples or seeds: entity, relation, value , where the relation is one of the target relations.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Our document-level distant supervision assumption is that if entity and value are found in a document graph (see section 3), and there is a path connecting them, then the document expresses the relation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Relation candidates gathering. From a seed triple, we retrieve candidate documents that contain both the entity and value, within a span of 20 tokens, using a standard IR approach. Then, entity and value are matched to the document graph representation. We first use approximate string comparison to find nodes matching the seed entity. After an entity node has been found we use local breadth-firstsearch (BFS) to find a matching value and the shortest connecting path between them. We enforce the Named Entity type of entity and value to match a expected type, predefined for the relation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Our procedure traverses the document graph looking for entity and value nodes meeting those conditions; when found, we generate features for a positive example for the relation 2 . If we encounter a node that matches the expected NE type of the relation, but does not match the seed value, we generate a negative example for that relation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Training. From positive and negative examples, we generate binary features; some of them are inspired by previous work (Surdeanu and Ciaramita, 2007; Mintz et al., 2009; Riedel et al., 2010; Surdeanu et al., 2010) , and others are novel, taking advantage of our graph representation. Table 1 summarizes our choice of features. Features appearing in less than 5 training examples were discarded.", |
| "cite_spans": [ |
| { |
| "start": 119, |
| "end": 149, |
| "text": "(Surdeanu and Ciaramita, 2007;", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 150, |
| "end": 169, |
| "text": "Mintz et al., 2009;", |
| "ref_id": "BIBREF14" |
| }, |
| { |
| "start": 170, |
| "end": 190, |
| "text": "Riedel et al., 2010;", |
| "ref_id": "BIBREF18" |
| }, |
| { |
| "start": 191, |
| "end": 213, |
| "text": "Surdeanu et al., 2010)", |
| "ref_id": "BIBREF22" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 284, |
| "end": 291, |
| "text": "Table 1", |
| "ref_id": "TABREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Relation instance extraction. Given an input entity and a target relation, we aim at finding a filler value for a relation instance. This task is known as Slot Filling. From the set of retrieved documents relevant to the query entity, represented as document graphs, Feature name Description path dependency path between ENTITY and VALUE in the sentence X-annotation NE annotations for X X-pos Part-of-speech annotations for X X-gov Governor of X in the dependency path X-mod Modifiers of X in the dependency path X-has age X is a NE, with an age attribute X-has class-C X is a NE, with a class C X-property-P X is a NE, and it has a property P X-has-Y X is a NE, with a possessive relation with another NE, Y X-is-Y X is a NE, in a copula with another NE, Y X-gender-G X is a NE, and it has gender G V -tense", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Tense of the verb V in the path V -aspect Aspect of the verb V in the path V -polarity Polarity (positive or negative) of the verb V we locate matching entities and start a local BFS of candidate values, generating for them an unlabelled example. For each of the relations to extract, a binary classifier (extractor) decides whether the example is a valid relation instance. For each particular relation classifier, only candidates with the expected entity and value types for the relation were used in the application phase. Each extractor was a SVM classifier with linear kernel (Joachims, 2002) . All learning parameters were set to their default values. The classification process yields a predicted class label, plus a real number indicating the margin. We performed an aggregation phase to sum the margins over distinct occurrences of the same extracted value. The rationale is that when the same value is extracted from more than one document, we should accumulate that evidence.", |
| "cite_spans": [ |
| { |
| "start": 581, |
| "end": 597, |
| "text": "(Joachims, 2002)", |
| "ref_id": "BIBREF9" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "The output of this phase is the set of extracted relations (positive for each of the classifiers), plus the documents where the same fact was detected (supporting documents).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distant Supervised Relation Extraction", |
| "sec_num": "4" |
| }, |
| { |
| "text": "In this section, we propose and discuss a unified methodological approach for temporal anchoring of relations. We assume the input is a relation instance and a set of supporting documents. The task is establishing a imprecise temporal anchor interval for the relation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "We present a four-step methodological approach:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "(1) representation of intra-document temporal information; (2) selection of relevant temporal information for the relation; (3) mapping of the link between relational fact and temporal information into an interval; and (4) aggregation of imprecise intervals.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Temporal representation. The first methodological step is to obtain and represent the available intradocument temporal information; the input is a document, and the task is to identify temporal signals and possible links among them. We use the term link for a relation between a temporal expression (a date) and an event; we want to avoid confusion with the term relation (a relational fact extracted from text).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "In our particular implementation: \u2022 We use TARSQI to extract temporal expressions and link them to events. In particular, TARSQI uses the following temporal links: included, simultaneous, after, before, begun by or ended. \u2022 We focus also on the syntactic pattern [Eventpreposition-Time] within the lexical context of the candidate entity and value. \u2022 Both are normalized into one from a set of predefined temporal links: within, throughout, beginning, ending, after and before.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Selection of temporal evidence. For each document and relational instance, we have to select those temporal expressions that are relevant. a. Document-level metadata. The default value we use is the document creation time (DCT), if available. The underlying assumption is that there is a within link from each fact expressed in the text and the document creation time. b. Temporal expressions. Temporal evidence comes also from the temporal expressions present in the context of a relation. In our particular implementation, we followed a straightforward approach, looking for the time expression closest in the document graph to the shortest path between the entity and value nodes. This search is performed via a limited depth BFS, starting from the nodes in the path, in order from value to entity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Mapping of temporal links into intervals. The third step is deciding how a relational fact and its relevant temporal information are themselves related.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "We have to map this information, expressed in text, to a temporal representation. We will use the imprecise anchor intervals described is section 2. Let T be a temporal expression identified in the document or its metadata. Now, the mapping of temporal constraints depends on the temporal link to the time expression identified; also, the semantics of the event have to be considered in order to decide the time period associated to a relation instance. This step is important because the event could refer just to the beginning of the relation, its ending, or both. For instance, it is obvious that having the event marry is different to having the event divorce, when deciding the temporal constraints associated to the spouse relation. Table 2 shows our particular mapping between temporal links and constraints. In particular, for the default document creation time, we suppose that a relation which appears in a document with creation time d held true at least in that date; that is, we are assuming a within link, and we map t 2 = d, t 3 = d.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 739, |
| "end": 746, |
| "text": "Table 2", |
| "ref_id": "TABREF4" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Inter-document temporal evidence aggregation. The last step is aggregating all the time constraints found for the same relation and value across different documents. If we found that a relation started after two dates d and d , where d > d, the closest constraint to the real start of the relation is d . Mapped to temporal constraints, it means that we would choose the biggest t 1 possible. Following the same reasoning, we would want to maximize t 3 . On the other side, when a relation started before two dates d 2 and d 2 , where d 2 > d 2 , the closest constraint is d 2 and we would choose the smallest t 2 . In summary, we will maximize t 1 and t 3 and minimize t 2 and t 4 , so we will narrow the margins.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Temporal Anchoring of Relations", |
| "sec_num": "5" |
| }, |
| { |
| "text": "We have used for our evaluation the dataset compiled within the TAC-KBP 2011 Temporal Slot Filling Task (Ji et al., 2011) . We employed as initial KB the one distributed to participants in the task, which has been compiled from Wikipedia infoboxes. It contains 898 triples entity, slot type, value for 100 different entities and up to 8 different slots (relations) per entity 3 . This gold standard contains the correct responses pooled from the participant systems plus a set of responses manually found by annotators. Each triple has associated a temporal anchor. The relations had to be extracted from a domain-general collection of 1.7 million documents. Our system was one of the five that took part in the task.We have evaluated the overall system and the two main components of the architecture: Relation Extraction, and Temporal Anchoring of the relations. Due to space limitations, the description of our implementation is very concise; refer to (Garrido et al., 2011) for further details.", |
| "cite_spans": [ |
| { |
| "start": 104, |
| "end": 121, |
| "text": "(Ji et al., 2011)", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 955, |
| "end": 977, |
| "text": "(Garrido et al., 2011)", |
| "ref_id": "BIBREF6" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation", |
| "sec_num": "6" |
| }, |
| { |
| "text": "System response in the relation extraction step consists in a set of triples entity, slot type, value . Performance is measured using precision, recall and F-measure (harmonic mean) with respect to the 898 triples in the key. Target relations (slots) are potentially list-valued, that is, more than one value can be valid for a relation (possibly at different points in time). Only correct values yield any score, and redundant triples are ignored.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Relation Extraction", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "Experiments. We run two different system settings for the relation extraction step. They differ in the document representation used (detailed in section3), in order to empirically assess whether clustering of discourse referents into single nodes benefits the extraction. In SETTING 1, each document is represented as a document graph, G D , while in SETTING 2 collapsed document graph representation, G C , is employed. Table 3 in the column Relation Extraction. Both settings have a similar performance with a slight increase in the case of graphs with clustered referents. Although precision is close to 0.5, recall is lower than 0.1. We have studied the limits of the assumptions our approach is based on. First, our standard retrieval component performance limits the overall system's. As a matter of example, if we retrieve the first 100 documents per entity, we find relevant documents only for 62% of the triples in the key. This number means that no matter how good relation extraction method is, 38% of relations will not be found.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 421, |
| "end": 428, |
| "text": "Table 3", |
| "ref_id": "TABREF6" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Evaluation of Relation Extraction", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "Second, the distant supervision assumption underlying our approach is that for a seed relation instance entity, relation, value , any textual mention of entity and value expresses the relation. It has been shown that this assumption is more often violated when training knowledge base and document collection are of different type, e.g. Wikipedia and news-wire (Riedel et al., 2010) . We have realized that a more determinant factor is the relation itself and the type of arguments it takes. We randomly sampled 100 training examples per relation, and manually inspected them to assess if they were indeed mentions of the relation. While for the relation cities of residence only 30% of the training examples are expressing the relation, for spouse the number goes up to 59%. For title, up to 90% of the examples are correct. This fact explains, at least partially, the zeros we obtain for some relations.", |
| "cite_spans": [ |
| { |
| "start": 361, |
| "end": 382, |
| "text": "(Riedel et al., 2010)", |
| "ref_id": "BIBREF18" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Results. Results are shown in", |
| "sec_num": null |
| }, |
| { |
| "text": "Under the evaluation metrics proposed by TAC-KBP 2011, if the value of the relation instance is judged as correct, the score for temporal anchoring depends on how well the returned interval matches the one provided in the key. More precisely, let the correct imprecise anchor interval in the gold standard key be S k = (k 1 , k 2 , k 3 , k 4 ) and the system response be S = (r 1 , r 2 , r 3 , r 4 ). The absence of a constraint in t 1 or t 3 is treated as a value of \u2212\u221e; the absence of a constraint in t 2 or t 4 is treated as a value of +\u221e. Then, let", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "d i = |k i \u2212 r i |, for i \u2208 1, .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": ". . , 4, be the difference, a real number measured in years. The score for the system response is:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "Q(S) = 1 4 4 i=1 1 1 + d i", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "The score for a target relation Q(r) is computed by summing Q(S) over all unique instances of the relation whose value is correct. If the gold standard contains N responses, and the system output M responses, then precision is: P = Q(r)/M , and recall: R = Q(r)/N ; F 1 is the harmonic mean of P and R.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "Experiments. We evaluated two different settings for the temporal anchoring step; both use the collapsed document graph representation, G C (SETTING 2). The goal of the experiment is twofold. First, test the strength of the document creation time as evidence for temporal anchoring. Second, test how hard this metadata-level baseline is to beat using contextual temporal expressions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "The SETTING 2-I assumes a within temporal link between the document creation time and any relation expressed inside the document, and aggregates this information across the documents that we have identified as supporting the relation. The SETTING 2-II considers documents content in order to extract temporal links from the context of the text that expresses the relation. If no temporal expression is found, the date of the document is used as default. Temporal links from all supporting documents are mapped into intervals and aggregated as detailed in section 5.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "The performance on relation extraction is an upper bound for temporal anchoring, attainable if temporal anchoring is perfect. Thus, we also evaluate the temporal anchoring performance as the percentage the final system achieves with respect to the relation extraction upper bound. Table 3 under column Temporal Anchoring. They are low, due to the upper bound that error propagation in candidate retrieval and relation extraction imposes upon this step: temporally anchoring alone achives 69% of its upper bound. This value corresponds to the baseline SET-TING 2-I, showing its strength. The difference with SETTING 2-II shows that this baseline is difficult to beat by considering temporal evidence inside the document content. There is a reason for this. The temporal link mapping into time intervals does not depend only on the type of link, but also on the semantics of the text that expresses the relation as we pointed out above. We have to decide how to transform the link between relation and temporal expression into a temporal interval. Learning a model for this is a hard open research problem that has a strong adversary in the baseline proposed. Temporal Anchoring SETTING 1 SETTING 2 SETTING 2-I SETTING 2-II P R F P R F P R F % P R F % (1) 0 0 0 0 0 0 0 0 0 0 0 0 Table 4 : System ID, number of filled responses of the system, precision, recall and F measure.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 281, |
| "end": 288, |
| "text": "Table 3", |
| "ref_id": "TABREF6" |
| }, |
| { |
| "start": 1158, |
| "end": 1308, |
| "text": "Temporal Anchoring SETTING 1 SETTING 2 SETTING 2-I SETTING 2-II P R F P R F P R F % P R F % (1) 0 0 0 0 0 0 0 0 0 0 0 0", |
| "ref_id": "TABREF2" |
| }, |
| { |
| "start": 1309, |
| "end": 1316, |
| "text": "Table 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Evaluation of Temporal Anchoring", |
| "sec_num": "6.2" |
| }, |
| { |
| "text": "0 0 (2) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (3) 0.33 0.02 0.03 0 0 0 0 0 0 0 0 0 0 0 (4) 0", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Relation Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "Our approach was compared with the other four participants at the KBP Temporal Slot Filling Task 2011. Table 4 shows results sorted by F-measure in comparison to our two settings (described above). These official results correspond to a previous dataset containing 712 triples 4 . As shown in column Filled our approach returns less triples than other systems, explaining low recall. However, our system achieves the highest precision for the complete task of temporally anchored relation extraction. Despite low recall, our system obtains the third best F 1 value. This is a very promising result, since several directions can be explored to consider more candidates and increase recall.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 103, |
| "end": 110, |
| "text": "Table 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Comparative Evaluation", |
| "sec_num": "6.3" |
| }, |
| { |
| "text": "Compiling a Knowledge Base of temporally anchored facts is an open research challenge . Despite the vast amount of research focusing on understanding temporal expressions and 4 Slot-fillers from human assessors were not considered their relation to events in natural language, the complete problem of temporally anchored relation extraction remains relatively unexplored. Also, while much research has focused on single-document extraction, it seems clear that extracting temporally anchored relations needs the aggregation of evidences across multiple documents.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Work", |
| "sec_num": "7" |
| }, |
| { |
| "text": "There have been attempts to extend an existing knowledge base. Wang et al. (2010) use regular expressions to mine Wikipedia infoboxes and categories and it is not suited for unrestricted text. An earlier attempt (Zhang et al., 2008) , is specific for business and difficult to generalize to other relations. Two recent promising works are more related to our research. Wang et al. (2011) uses manually defined patterns to collect candidate facts and explicit dates, and re-rank them using a graph label propagation algorithm; their approach is complementary to ours, as our aim is not to harvest temporal facts but to extract the relations in which a query entity takes part; unlike us, they require entity, value, and a explicit date to appear in the same sentence. Talukdar et al. (2012) focus on the partial task of temporally anchoring already known facts, showing the usefulness of the document creation time as temporal signal, aggregated across documents.", |
| "cite_spans": [ |
| { |
| "start": 63, |
| "end": 81, |
| "text": "Wang et al. (2010)", |
| "ref_id": "BIBREF27" |
| }, |
| { |
| "start": 212, |
| "end": 232, |
| "text": "(Zhang et al., 2008)", |
| "ref_id": "BIBREF31" |
| }, |
| { |
| "start": 369, |
| "end": 387, |
| "text": "Wang et al. (2011)", |
| "ref_id": "BIBREF28" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Work", |
| "sec_num": "7" |
| }, |
| { |
| "text": "Earlier work has dealt mainly with partial aspects of the problem. The TempEval community focused on the classification of the temporal links between pairs of events, or an event and a temporal expression; using shallow features (Mani et al., 2003; Lapata and Lascarides, 2004; Chambers et al., 2007) , or syntactic-based structured features (Bethard and Martin, 2007; Pu\u015fca\u015fu, 2007; Cheng et al., 2007) .", |
| "cite_spans": [ |
| { |
| "start": 229, |
| "end": 248, |
| "text": "(Mani et al., 2003;", |
| "ref_id": "BIBREF13" |
| }, |
| { |
| "start": 249, |
| "end": 277, |
| "text": "Lapata and Lascarides, 2004;", |
| "ref_id": "BIBREF11" |
| }, |
| { |
| "start": 278, |
| "end": 300, |
| "text": "Chambers et al., 2007)", |
| "ref_id": "BIBREF4" |
| }, |
| { |
| "start": 342, |
| "end": 368, |
| "text": "(Bethard and Martin, 2007;", |
| "ref_id": "BIBREF2" |
| }, |
| { |
| "start": 369, |
| "end": 383, |
| "text": "Pu\u015fca\u015fu, 2007;", |
| "ref_id": "BIBREF16" |
| }, |
| { |
| "start": 384, |
| "end": 403, |
| "text": "Cheng et al., 2007)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Work", |
| "sec_num": "7" |
| }, |
| { |
| "text": "Aggregating evidence across different documents to temporally anchor facts has been explored in settings different to Information Extraction, such as answering of definition questions (Pa\u015fca, 2008) or extracting possible dates of well-known historical events (Schockaert et al., 2010) . Temporal inference or reasoning to solve conflicting temporal expressions and induce temporal order of events has been used in TempEval (Tatu and Srikanth, 2008; Yoshikawa et al., 2009) and ACE (Gupta and Ji, 2009) tasks, but focused on single-document extraction. Ling et al. (2010) , use cross-event joint inference to extract temporal facts, but only inside a single document.", |
| "cite_spans": [ |
| { |
| "start": 184, |
| "end": 197, |
| "text": "(Pa\u015fca, 2008)", |
| "ref_id": "BIBREF15" |
| }, |
| { |
| "start": 259, |
| "end": 284, |
| "text": "(Schockaert et al., 2010)", |
| "ref_id": "BIBREF20" |
| }, |
| { |
| "start": 423, |
| "end": 448, |
| "text": "(Tatu and Srikanth, 2008;", |
| "ref_id": "BIBREF24" |
| }, |
| { |
| "start": 449, |
| "end": 472, |
| "text": "Yoshikawa et al., 2009)", |
| "ref_id": "BIBREF30" |
| }, |
| { |
| "start": 481, |
| "end": 501, |
| "text": "(Gupta and Ji, 2009)", |
| "ref_id": "BIBREF7" |
| }, |
| { |
| "start": 552, |
| "end": 570, |
| "text": "Ling et al. (2010)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Work", |
| "sec_num": "7" |
| }, |
| { |
| "text": "Evaluation campaigns, such as ACE and TAC-KBP 2011 have had an important role in promoting this research. While ACE required only to identify time expressions and classify their relation to events, KBP requires to infer explicitly the start/end time of relations, which is a realistic approach in the context of building time-aware knowledge bases. KBP represents an important step for the evaluation of temporal information extraction systems. In general, the participant systems adapted existing slot filling systems, adding a temporal classification component: distant supervised (Chen et al., 2010; Surdeanu et al., 2010) on manually-defined patterns (Byrne and Dunnion, 2010) .", |
| "cite_spans": [ |
| { |
| "start": 583, |
| "end": 602, |
| "text": "(Chen et al., 2010;", |
| "ref_id": "BIBREF5" |
| }, |
| { |
| "start": 603, |
| "end": 625, |
| "text": "Surdeanu et al., 2010)", |
| "ref_id": "BIBREF22" |
| }, |
| { |
| "start": 655, |
| "end": 680, |
| "text": "(Byrne and Dunnion, 2010)", |
| "ref_id": "BIBREF3" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Work", |
| "sec_num": "7" |
| }, |
| { |
| "text": "This paper introduces the problem of extracting, from unrestricted natural language text, relational knowledge anchored to a temporal span, aggregating temporal evidence from a collection of documents. Although compiling time-aware knowledge bases is an important open challenge , it has remained unexplored until very recently (Wang et al., 2011; Talukdar et al., 2012) .", |
| "cite_spans": [ |
| { |
| "start": 328, |
| "end": 347, |
| "text": "(Wang et al., 2011;", |
| "ref_id": "BIBREF28" |
| }, |
| { |
| "start": 348, |
| "end": 370, |
| "text": "Talukdar et al., 2012)", |
| "ref_id": "BIBREF23" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "We have elucidated the two challenges of the task, namely relation extraction and temporal anchoring of the extracted relations.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "We have studied how, in a pipeline architecture, the propagation of errors limits the overall system's performance. The performance attainable in the full task is limited by the quality of the output of the three main phases: retrieval of candidate passages/ documents, extraction of relations and temporal anchoring of those.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "We have also studied the limits of the distant supervision approach to relation extraction, showing empirically that its performance depends not only on the nature of reference knowledge base and document corpus (Riedel et al., 2010) , but also on the relation to be extracted. Given a relation between two arguments, if it is not dominant among textual expressions of those arguments, the distant supervision assumption will be more often violated.", |
| "cite_spans": [ |
| { |
| "start": 212, |
| "end": 233, |
| "text": "(Riedel et al., 2010)", |
| "ref_id": "BIBREF18" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "We have introduced a novel graph-based document level representation, that has allowed us to generate new features for the task of relation extraction, capturing long distance structured contexts. Our results show how, in a document level syntactic representation, it yields better results to collapse coreferent nodes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "We have presented a methodological approach to temporal anchoring composed of: (1) intradocument temporal information representation; (2) selection of relation-dependent relevant temporal information; (3) mapping of temporal links to an interval representation; and (4) aggregation of imprecise intervals.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "Our proposal has been evaluated within a framework that allows for comparability. It has been able to extract temporally anchored relational information with the highest precision among the participant systems taking part in the competitive evaluation TAC-KBP 2011.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "For the temporal anchoring sub-problem, we have demonstrated the strength of the document creation time as a temporal signal. It is possible to achieve a performance of 69% of the upper-bound imposed by relation extraction by assuming that any relation mentioned in a document held at the document creation time (there is a within link between the relational fact and the document creation time). This baseline has proved stronger than extracting and analyzing the temporal expressions present in the document content.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "8" |
| }, |
| { |
| "text": "Most chunks consist in one word; we join words into a chunk (and a node) in two cases: a multi-word named entity and a verb and its auxiliaries.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "From the collapsed document graph representation we obtained an average of 9213 positive training examples per slot; from the uncollapsed document graph, a slightly lower average of 8178.5 positive examples per slot.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "There are 7 person relations: cities of residence, stateorprovinces of residence, countries of residence, employee of, member of, title, spouse, and an organization relation: top members/employees.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "This work has been partially supported by the Spanish Ministry of Science and Innovation, through the project Holopedia (TIN2010-21128-C02), and the Regional Government of Madrid, through the project MA2VICMR (S2009/TIC1542).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgments", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Stanford-UBC at TAC-KBP", |
| "authors": [ |
| { |
| "first": "Eneko", |
| "middle": [], |
| "last": "Agirre", |
| "suffix": "" |
| }, |
| { |
| "first": "Angel", |
| "middle": [ |
| "X" |
| ], |
| "last": "Chang", |
| "suffix": "" |
| }, |
| { |
| "first": "Daniel", |
| "middle": [ |
| "S" |
| ], |
| "last": "Jurafsky", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
| ], |
| "last": "Manning", |
| "suffix": "" |
| }, |
| { |
| "first": "Valentin", |
| "middle": [ |
| "I" |
| ], |
| "last": "Spitkovsky", |
| "suffix": "" |
| }, |
| { |
| "first": "Eric", |
| "middle": [], |
| "last": "Yeh", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Eneko Agirre, Angel X. Chang, Daniel S. Jurafsky, Christopher D. Manning, Valentin I. Spitkovsky, and Eric Yeh. 2009. Stanford-UBC at TAC-KBP. In TAC 2009, November.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Maintaining knowledge about temporal intervals", |
| "authors": [ |
| { |
| "first": "James", |
| "middle": [ |
| "F" |
| ], |
| "last": "Allen", |
| "suffix": "" |
| } |
| ], |
| "year": 1983, |
| "venue": "Commun. ACM", |
| "volume": "26", |
| "issue": "", |
| "pages": "832--843", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "James F. Allen. 1983. Maintaining knowledge about temporal intervals. Commun. ACM, 26:832-843, November.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Cu-tmp: temporal relation classification using syntactic and semantic features", |
| "authors": [ |
| { |
| "first": "Steven", |
| "middle": [], |
| "last": "Bethard", |
| "suffix": "" |
| }, |
| { |
| "first": "James", |
| "middle": [ |
| "H" |
| ], |
| "last": "Martin", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "Proceedings of the 4th International Workshop on Semantic Evaluations, SemEval '07", |
| "volume": "", |
| "issue": "", |
| "pages": "129--132", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Steven Bethard and James H. Martin. 2007. Cu-tmp: temporal relation classification using syntactic and se- mantic features. In Proceedings of the 4th Interna- tional Workshop on Semantic Evaluations, SemEval '07, pages 129-132, Stroudsburg, PA, USA. Associ- ation for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "UCD IIRG at TAC 2010 KBP Slot Filling Task", |
| "authors": [ |
| { |
| "first": "Lorna", |
| "middle": [], |
| "last": "Byrne", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [], |
| "last": "Dunnion", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the Third Text Analysis Conference", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Lorna Byrne and John Dunnion. 2010. UCD IIRG at TAC 2010 KBP Slot Filling Task. In Proceedings of the Third Text Analysis Conference (TAC 2010). NIST, November.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Classifying temporal relations between events", |
| "authors": [ |
| { |
| "first": "Nathanael", |
| "middle": [], |
| "last": "Chambers", |
| "suffix": "" |
| }, |
| { |
| "first": "Shan", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Jurafsky", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL '07", |
| "volume": "", |
| "issue": "", |
| "pages": "173--176", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nathanael Chambers, Shan Wang, and Dan Jurafsky. 2007. Classifying temporal relations between events. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL '07, pages 173-176, Stroudsburg, PA, USA. As- sociation for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "CUNY-BLENDER TAC-KBP2010: Entity linking and slot filling system description", |
| "authors": [ |
| { |
| "first": "Zheng", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Suzanne", |
| "middle": [], |
| "last": "Tamang", |
| "suffix": "" |
| }, |
| { |
| "first": "Adam", |
| "middle": [], |
| "last": "Lee", |
| "suffix": "" |
| }, |
| { |
| "first": "Xiang", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Wen-Pin", |
| "middle": [], |
| "last": "Lin", |
| "suffix": "" |
| }, |
| { |
| "first": "Matthew", |
| "middle": [], |
| "last": "Snover", |
| "suffix": "" |
| }, |
| { |
| "first": "Javier", |
| "middle": [], |
| "last": "Artiles", |
| "suffix": "" |
| }, |
| { |
| "first": "Marissa", |
| "middle": [], |
| "last": "Passantino", |
| "suffix": "" |
| }, |
| { |
| "first": "Heng", |
| "middle": [], |
| "last": "Ji", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "Proceedings of the Third Text Analysis Conference (TAC 2010). NIST, November. Yuchang Cheng, Masayuki Asahara, and Yuji Matsumoto", |
| "volume": "", |
| "issue": "", |
| "pages": "245--248", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Zheng Chen, Suzanne Tamang, Adam Lee, Xiang Li, Wen-Pin Lin, Matthew Snover, Javier Artiles, Marissa Passantino, and Heng Ji. 2010. CUNY-BLENDER TAC-KBP2010: Entity linking and slot filling system description. In Proceedings of the Third Text Analysis Conference (TAC 2010). NIST, November. Yuchang Cheng, Masayuki Asahara, and Yuji Mat- sumoto. 2007. Naist.japan: temporal relation identifi- cation using dependency parsed tree. In Proceedings of the 4th International Workshop on Semantic Evalu- ations, SemEval '07, pages 245-248, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "A distant supervised learning system for the TAC-KBP Slot Filling and Temporal Slot Filling Tasks", |
| "authors": [ |
| { |
| "first": "Guillermo", |
| "middle": [], |
| "last": "Garrido", |
| "suffix": "" |
| }, |
| { |
| "first": "Bernardo", |
| "middle": [], |
| "last": "Cabaleiro", |
| "suffix": "" |
| }, |
| { |
| "first": "Anselmo", |
| "middle": [], |
| "last": "Peas", |
| "suffix": "" |
| }, |
| { |
| "first": "Rodrigo", |
| "middle": [], |
| "last": "Varo", |
| "suffix": "" |
| }, |
| { |
| "first": "Damiano", |
| "middle": [], |
| "last": "Spina", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Text Analysis Conference, TAC 2011 Proceedings Papers", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Guillermo Garrido, Bernardo Cabaleiro, Anselmo Peas, varo Rodrigo, and Damiano Spina. 2011. A distant supervised learning system for the TAC-KBP Slot Fill- ing and Temporal Slot Filling Tasks. In Text Analysis Conference, TAC 2011 Proceedings Papers.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "Predicting unknown time arguments based on cross-event propagation", |
| "authors": [ |
| { |
| "first": "Prashant", |
| "middle": [], |
| "last": "Gupta", |
| "suffix": "" |
| }, |
| { |
| "first": "Heng", |
| "middle": [], |
| "last": "Ji", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, ACLShort '09", |
| "volume": "", |
| "issue": "", |
| "pages": "369--372", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Prashant Gupta and Heng Ji. 2009. Predicting un- known time arguments based on cross-event propaga- tion. In Proceedings of the ACL-IJCNLP 2009 Con- ference Short Papers, ACLShort '09, pages 369-372, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "Overview of the tac2011 knowledge base population track", |
| "authors": [ |
| { |
| "first": "Heng", |
| "middle": [], |
| "last": "Ji", |
| "suffix": "" |
| }, |
| { |
| "first": "Ralph", |
| "middle": [], |
| "last": "Grishman", |
| "suffix": "" |
| }, |
| { |
| "first": "Hoa", |
| "middle": [ |
| "Trang" |
| ], |
| "last": "Dang", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Text Analysis Conference, TAC 2011 Workshop, Notebook Papers", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Heng Ji, Ralph Grishman, and Hoa Trang Dang. 2011. Overview of the tac2011 knowledge base population track. In Text Analysis Conference, TAC 2011 Work- shop, Notebook Papers.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "Learning to Classify Text Using Support Vector Machines -Methods, Theory, and Algorithms. Kluwer/Springer. We used Joachim's SVMLight implementation", |
| "authors": [ |
| { |
| "first": "T", |
| "middle": [], |
| "last": "Joachims", |
| "suffix": "" |
| } |
| ], |
| "year": 2002, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "T. Joachims. 2002. Learning to Classify Text Us- ing Support Vector Machines -Methods, Theory, and Algorithms. Kluwer/Springer. We used Joachim's SVMLight implementation available at http:// svmlight.joachims.org/.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Accurate unlexicalized parsing", |
| "authors": [ |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Klein", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
| ], |
| "last": "Manning", |
| "suffix": "" |
| } |
| ], |
| "year": 2003, |
| "venue": "ACL 2003", |
| "volume": "", |
| "issue": "", |
| "pages": "423--430", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In ACL 2003, pages 423-430.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "Inferring sentence-internal temporal relations", |
| "authors": [ |
| { |
| "first": "Mirella", |
| "middle": [], |
| "last": "Lapata", |
| "suffix": "" |
| }, |
| { |
| "first": "Alex", |
| "middle": [], |
| "last": "Lascarides", |
| "suffix": "" |
| } |
| ], |
| "year": 2004, |
| "venue": "HLT", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Mirella Lapata and Alex Lascarides. 2004. Inferring sentence-internal temporal relations. In HLT 2004.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Temporal information extraction", |
| "authors": [ |
| { |
| "first": "Xiao", |
| "middle": [], |
| "last": "Ling", |
| "suffix": "" |
| }, |
| { |
| "first": "S", |
| "middle": [], |
| "last": "Daniel", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Weld", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Xiao Ling and Daniel S. Weld. 2010. Temporal informa- tion extraction. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10).", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Inferring temporal ordering of events in news", |
| "authors": [ |
| { |
| "first": "Inderjeet", |
| "middle": [], |
| "last": "Mani", |
| "suffix": "" |
| }, |
| { |
| "first": "Barry", |
| "middle": [], |
| "last": "Schiffman", |
| "suffix": "" |
| }, |
| { |
| "first": "Jianping", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| } |
| ], |
| "year": 2003, |
| "venue": "NAACL-Short'03", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Inderjeet Mani, Barry Schiffman, and Jianping Zhang. 2003. Inferring temporal ordering of events in news. In NAACL-Short'03.", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "Distant supervision for relation extraction without labeled data", |
| "authors": [ |
| { |
| "first": "Mike", |
| "middle": [], |
| "last": "Mintz", |
| "suffix": "" |
| }, |
| { |
| "first": "Steven", |
| "middle": [], |
| "last": "Bills", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "ACL 2009", |
| "volume": "", |
| "issue": "", |
| "pages": "1003--1011", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Mike Mintz, Steven Bills, Rion Snow, and Dan Juraf- sky. 2009. Distant supervision for relation extraction without labeled data. In ACL 2009, pages 1003-1011, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF15": { |
| "ref_id": "b15", |
| "title": "Answering Definition Questions via Temporally-Anchored Text Snippets", |
| "authors": [ |
| { |
| "first": "M", |
| "middle": [], |
| "last": "Pa\u015fca", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proc. of IJC-NLP2008", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "M Pa\u015fca. 2008. Answering Definition Questions via Temporally-Anchored Text Snippets. Proc. of IJC- NLP2008.", |
| "links": null |
| }, |
| "BIBREF16": { |
| "ref_id": "b16", |
| "title": "Wvali: temporal relation identification by syntactico-semantic analysis", |
| "authors": [ |
| { |
| "first": "Georgiana", |
| "middle": [], |
| "last": "Pu\u015fca\u015fu", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "Proceedings of the 4th International Workshop on Semantic Evaluations, SemEval '07", |
| "volume": "", |
| "issue": "", |
| "pages": "484--487", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Georgiana Pu\u015fca\u015fu. 2007. Wvali: temporal relation identification by syntactico-semantic analysis. In Pro- ceedings of the 4th International Workshop on Se- mantic Evaluations, SemEval '07, pages 484-487, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF17": { |
| "ref_id": "b17", |
| "title": "TempEval-2)", |
| "authors": [ |
| { |
| "first": "James", |
| "middle": [], |
| "last": "Pustejovsky", |
| "suffix": "" |
| }, |
| { |
| "first": "Marc", |
| "middle": [], |
| "last": "Verhagen", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, DEW '09", |
| "volume": "", |
| "issue": "", |
| "pages": "112--116", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "James Pustejovsky and Marc Verhagen. 2009. SemEval- 2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2). In Proceed- ings of the Workshop on Semantic Evaluations: Re- cent Achievements and Future Directions, DEW '09, pages 112-116, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF18": { |
| "ref_id": "b18", |
| "title": "Modeling relations and their mentions without labeled text", |
| "authors": [ |
| { |
| "first": "Sebastian", |
| "middle": [], |
| "last": "Riedel", |
| "suffix": "" |
| }, |
| { |
| "first": "Limin", |
| "middle": [], |
| "last": "Yao", |
| "suffix": "" |
| }, |
| { |
| "first": "Andrew", |
| "middle": [], |
| "last": "Mccallum", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Jos\u00e9 Balc\u00e1zar, Francesco Bonchi, Aristides Gionis, and Mich\u00e8le Sebag", |
| "volume": "6323", |
| "issue": "", |
| "pages": "148--163", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions with- out labeled text. In Jos\u00e9 Balc\u00e1zar, Francesco Bonchi, Aristides Gionis, and Mich\u00e8le Sebag, editors, Machine Learning and Knowledge Discovery in Databases, volume 6323 of LNCS, pages 148-163. Springer Berlin / Heidelberg.", |
| "links": null |
| }, |
| "BIBREF19": { |
| "ref_id": "b19", |
| "title": "Artificial Intelligence -A Modern Approach (3. internat. ed.). Pearson Education", |
| "authors": [ |
| { |
| "first": "J", |
| "middle": [], |
| "last": "Stuart", |
| "suffix": "" |
| }, |
| { |
| "first": "Peter", |
| "middle": [], |
| "last": "Russell", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Norvig", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Stuart J. Russell and Peter Norvig. 2010. Artificial Intel- ligence -A Modern Approach (3. internat. ed.). Pear- son Education.", |
| "links": null |
| }, |
| "BIBREF20": { |
| "ref_id": "b20", |
| "title": "Reasoning about fuzzy temporal information from the web: towards retrieval of historical events. Soft Computing -A Fusion of Foundations, Methodologies and Applications", |
| "authors": [ |
| { |
| "first": "Steven", |
| "middle": [], |
| "last": "Schockaert", |
| "suffix": "" |
| }, |
| { |
| "first": "Martine", |
| "middle": [ |
| "De" |
| ], |
| "last": "Cock", |
| "suffix": "" |
| }, |
| { |
| "first": "Etienne", |
| "middle": [ |
| "Kerre" |
| ], |
| "last": "", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "", |
| "volume": "14", |
| "issue": "", |
| "pages": "869--886", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Steven Schockaert, Martine De Cock, and Etienne Kerre. 2010. Reasoning about fuzzy temporal information from the web: towards retrieval of historical events. Soft Computing -A Fusion of Foundations, Method- ologies and Applications, 14:869-886.", |
| "links": null |
| }, |
| "BIBREF21": { |
| "ref_id": "b21", |
| "title": "Robust information extraction with perceptrons", |
| "authors": [ |
| { |
| "first": "Mihai", |
| "middle": [], |
| "last": "Surdeanu", |
| "suffix": "" |
| }, |
| { |
| "first": "Massimiliano", |
| "middle": [], |
| "last": "Ciaramita", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "ACE07", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Mihai Surdeanu and Massimiliano Ciaramita. 2007. Robust information extraction with perceptrons. In ACE07, March.", |
| "links": null |
| }, |
| "BIBREF22": { |
| "ref_id": "b22", |
| "title": "A simple distant supervision approach for the tac-kbp slot filling task", |
| "authors": [ |
| { |
| "first": "Mihai", |
| "middle": [], |
| "last": "Surdeanu", |
| "suffix": "" |
| }, |
| { |
| "first": "David", |
| "middle": [], |
| "last": "Mcclosky", |
| "suffix": "" |
| }, |
| { |
| "first": "Julie", |
| "middle": [], |
| "last": "Tibshirani", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [], |
| "last": "Bauer", |
| "suffix": "" |
| }, |
| { |
| "first": "Angel", |
| "middle": [ |
| "X" |
| ], |
| "last": "Chang", |
| "suffix": "" |
| }, |
| { |
| "first": "Valentin", |
| "middle": [ |
| "I" |
| ], |
| "last": "Spitkovsky", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
| ], |
| "last": "Manning", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the Third Text Analysis Conference", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Mihai Surdeanu, David McClosky, Julie Tibshirani, John Bauer, Angel X. Chang, Valentin I. Spitkovsky, and Christopher D. Manning. 2010. A simple distant supervision approach for the tac-kbp slot filling task. In Proceedings of the Third Text Analysis Conference (TAC 2010), Gaithersburg, Maryland, USA, Novem- ber. NIST.", |
| "links": null |
| }, |
| "BIBREF23": { |
| "ref_id": "b23", |
| "title": "Coupled temporal scoping of relational facts", |
| "authors": [ |
| { |
| "first": "Partha", |
| "middle": [ |
| "Pratim" |
| ], |
| "last": "Talukdar", |
| "suffix": "" |
| }, |
| { |
| "first": "Derry", |
| "middle": [], |
| "last": "Wijaya", |
| "suffix": "" |
| }, |
| { |
| "first": "Tom", |
| "middle": [], |
| "last": "Mitchell", |
| "suffix": "" |
| } |
| ], |
| "year": 2012, |
| "venue": "Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM), Seattle", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Partha Pratim Talukdar, Derry Wijaya, and Tom Mitchell. 2012. Coupled temporal scoping of relational facts. In Proceedings of the Fifth ACM International Confer- ence on Web Search and Data Mining (WSDM), Seat- tle, Washington, USA, February. Association for Com- puting Machinery.", |
| "links": null |
| }, |
| "BIBREF24": { |
| "ref_id": "b24", |
| "title": "Experiments with reasoning for temporal relations between events", |
| "authors": [ |
| { |
| "first": "Marta", |
| "middle": [], |
| "last": "Tatu", |
| "suffix": "" |
| }, |
| { |
| "first": "Munirathnam", |
| "middle": [], |
| "last": "Srikanth", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "COLING'08", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marta Tatu and Munirathnam Srikanth. 2008. Experi- ments with reasoning for temporal relations between events. In COLING'08.", |
| "links": null |
| }, |
| "BIBREF25": { |
| "ref_id": "b25", |
| "title": "Automating temporal annotation with TARSQI", |
| "authors": [ |
| { |
| "first": "Marc", |
| "middle": [], |
| "last": "Verhagen", |
| "suffix": "" |
| }, |
| { |
| "first": "Inderjeet", |
| "middle": [], |
| "last": "Mani", |
| "suffix": "" |
| }, |
| { |
| "first": "Roser", |
| "middle": [], |
| "last": "Sauri", |
| "suffix": "" |
| }, |
| { |
| "first": "Robert", |
| "middle": [], |
| "last": "Knippen", |
| "suffix": "" |
| }, |
| { |
| "first": "Jessica", |
| "middle": [], |
| "last": "Seok Bae Jang", |
| "suffix": "" |
| }, |
| { |
| "first": "Anna", |
| "middle": [], |
| "last": "Littman", |
| "suffix": "" |
| }, |
| { |
| "first": "John", |
| "middle": [], |
| "last": "Rumshisky", |
| "suffix": "" |
| }, |
| { |
| "first": "James", |
| "middle": [], |
| "last": "Phillips", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Pustejovsky", |
| "suffix": "" |
| } |
| ], |
| "year": 2005, |
| "venue": "ACLdemo'05", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marc Verhagen, Inderjeet Mani, Roser Sauri, Robert Knippen, Seok Bae Jang, Jessica Littman, Anna Rumshisky, John Phillips, and James Pustejovsky. 2005. Automating temporal annotation with TARSQI. In ACLdemo'05.", |
| "links": null |
| }, |
| "BIBREF26": { |
| "ref_id": "b26", |
| "title": "SemEval-2007 task 15: TempEval temporal relation identification", |
| "authors": [ |
| { |
| "first": "Marc", |
| "middle": [], |
| "last": "Verhagen", |
| "suffix": "" |
| }, |
| { |
| "first": "Robert", |
| "middle": [], |
| "last": "Gaizauskas", |
| "suffix": "" |
| }, |
| { |
| "first": "Frank", |
| "middle": [], |
| "last": "Schilder", |
| "suffix": "" |
| }, |
| { |
| "first": "Mark", |
| "middle": [], |
| "last": "Hepple", |
| "suffix": "" |
| }, |
| { |
| "first": "Graham", |
| "middle": [], |
| "last": "Katz", |
| "suffix": "" |
| }, |
| { |
| "first": "James", |
| "middle": [], |
| "last": "Pustejovsky", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marc Verhagen, Robert Gaizauskas, Frank Schilder, Mark Hepple, Graham Katz, and James Pustejovsky. 2007. SemEval-2007 task 15: TempEval temporal re- lation identification. In SemEval'07.", |
| "links": null |
| }, |
| "BIBREF27": { |
| "ref_id": "b27", |
| "title": "Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia", |
| "authors": [ |
| { |
| "first": "Yafang", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Mingjie", |
| "middle": [], |
| "last": "Zhu", |
| "suffix": "" |
| }, |
| { |
| "first": "Lizhen", |
| "middle": [], |
| "last": "Qu", |
| "suffix": "" |
| }, |
| { |
| "first": "Marc", |
| "middle": [], |
| "last": "Spaniol", |
| "suffix": "" |
| }, |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the 13th International Conference on Extending Database Technology, EDBT '10", |
| "volume": "", |
| "issue": "", |
| "pages": "697--700", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Yafang Wang, Mingjie Zhu, Lizhen Qu, Marc Spaniol, and Gerhard Weikum. 2010. Timely YAGO: har- vesting, querying, and visualizing temporal knowledge from Wikipedia. In Proceedings of the 13th Inter- national Conference on Extending Database Technol- ogy, EDBT '10, pages 697-700, New York, NY, USA. ACM.", |
| "links": null |
| }, |
| "BIBREF28": { |
| "ref_id": "b28", |
| "title": "Harvesting facts from textual web sources by constrained label propagation", |
| "authors": [ |
| { |
| "first": "Yafang", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Bin", |
| "middle": [], |
| "last": "Yang", |
| "suffix": "" |
| }, |
| { |
| "first": "Lizhen", |
| "middle": [], |
| "last": "Qu", |
| "suffix": "" |
| }, |
| { |
| "first": "Marc", |
| "middle": [], |
| "last": "Spaniol", |
| "suffix": "" |
| }, |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11", |
| "volume": "", |
| "issue": "", |
| "pages": "837--846", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Yafang Wang, Bin Yang, Lizhen Qu, Marc Spaniol, and Gerhard Weikum. 2011. Harvesting facts from textual web sources by constrained label propagation. In Pro- ceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pages 837-846, New York, NY, USA. ACM.", |
| "links": null |
| }, |
| "BIBREF29": { |
| "ref_id": "b29", |
| "title": "Temporal knowledge for timely intelligence", |
| "authors": [ |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| }, |
| { |
| "first": "Srikanta", |
| "middle": [], |
| "last": "Bedathur", |
| "suffix": "" |
| }, |
| { |
| "first": "Ralf", |
| "middle": [], |
| "last": "Schenkel", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Enabling Real-Time Business Intelligence", |
| "volume": "84", |
| "issue": "", |
| "pages": "1--6", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Gerhard Weikum, Srikanta Bedathur, and Ralf Schenkel. 2011. Temporal knowledge for timely intelligence. In Malu Castellanos, Umeshwar Dayal, Volker Markl, Wil Aalst, John Mylopoulos, Michael Rosemann, Michael J. Shaw, and Clemens Szyperski, editors, En- abling Real-Time Business Intelligence, volume 84 of Lecture Notes in Business Information Processing, pages 1-6. Springer Berlin Heidelberg.", |
| "links": null |
| }, |
| "BIBREF30": { |
| "ref_id": "b30", |
| "title": "Jointly identifying temporal relations with Markov Logic", |
| "authors": [ |
| { |
| "first": "Katsumasa", |
| "middle": [], |
| "last": "Yoshikawa", |
| "suffix": "" |
| }, |
| { |
| "first": "Sebastian", |
| "middle": [], |
| "last": "Riedel", |
| "suffix": "" |
| }, |
| { |
| "first": "Masayuki", |
| "middle": [], |
| "last": "Asahara", |
| "suffix": "" |
| }, |
| { |
| "first": "Yuji", |
| "middle": [], |
| "last": "Matsumoto", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP", |
| "volume": "1", |
| "issue": "", |
| "pages": "405--413", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Katsumasa Yoshikawa, Sebastian Riedel, Masayuki Asa- hara, and Yuji Matsumoto. 2009. Jointly identifying temporal relations with Markov Logic. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 -Volume 1, ACL '09, pages 405-413, Stroudsburg, PA, USA. Association for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF31": { |
| "ref_id": "b31", |
| "title": "TOB: Timely ontologies for business relations", |
| "authors": [ |
| { |
| "first": "Qi", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Fabian", |
| "middle": [ |
| "M" |
| ], |
| "last": "Suchanek", |
| "suffix": "" |
| }, |
| { |
| "first": "Lihua", |
| "middle": [], |
| "last": "Yue", |
| "suffix": "" |
| }, |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "11th International Workshop on the Web and Databases", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Qi Zhang, Fabian M. Suchanek, Lihua Yue, and Gerhard Weikum. 2008. TOB: Timely ontologies for business relations. In 11th International Workshop on the Web and Databases, WebDB.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF1": { |
| "num": null, |
| "type_str": "figure", |
| "uris": null, |
| "text": "System overview diagram." |
| }, |
| "TABREF2": { |
| "html": null, |
| "content": "<table/>", |
| "type_str": "table", |
| "num": null, |
| "text": "Features included in the model. X stands for ENTITY and VALUE. Verb features are generated from the verbs, V , identified in the path between ENTITY and VALUE." |
| }, |
| "TABREF3": { |
| "html": null, |
| "content": "<table><tr><td>Temporal link</td><td>Constraints mapping</td></tr><tr><td>Before</td><td>t4 = first</td></tr><tr><td>After</td><td>t1 = last</td></tr></table>", |
| "type_str": "table", |
| "num": null, |
| "text": "Within and Throughout t2 = first and t3 = last Beginning t1 = first and t2 = last Ending t3 = first and t4 = last" |
| }, |
| "TABREF4": { |
| "html": null, |
| "content": "<table/>", |
| "type_str": "table", |
| "num": null, |
| "text": "Mapping from time expression and temporal relation to temporal constraints." |
| }, |
| "TABREF6": { |
| "html": null, |
| "content": "<table><tr><td colspan=\"4\">: Results of experiments for each relation: (1) per:stateorprovinces of residence; (2) per:employee of; (3)</td></tr><tr><td colspan=\"4\">per:countries of residence; (4) per:member of; (5) per:title; (6) org:top members/employees; (7) per:spouse; (8)</td></tr><tr><td colspan=\"4\">per:cities of residence; (9) overall results (calculated as a micro-average).</td></tr><tr><td>System</td><td colspan=\"2\"># Filled Precision Recall</td><td>F1</td></tr><tr><td>BLENDER2</td><td>1206</td><td colspan=\"2\">0.1789 0.3030 0.2250</td></tr><tr><td>BLENDER1</td><td>1116</td><td colspan=\"2\">0.1796 0.2942 0.2231</td></tr><tr><td>BLENDER3</td><td>1215</td><td colspan=\"2\">0.1744 0.2976 0.2199</td></tr><tr><td>IIRG1</td><td>346</td><td colspan=\"2\">0.2457 0.1194 0.1607</td></tr><tr><td>Setting 2-1</td><td>167</td><td colspan=\"2\">0.2996 0.0703 0.1139</td></tr><tr><td>Setting 2-2</td><td>167</td><td colspan=\"2\">0.2596 0.0609 0.0986</td></tr><tr><td>Stanford 12</td><td>5140</td><td colspan=\"2\">0.0233 0.1680 0.0409</td></tr><tr><td>Stanford 11</td><td>4353</td><td colspan=\"2\">0.0238 0.1453 0.0408</td></tr><tr><td>USFD20112</td><td>328</td><td colspan=\"2\">0.0152 0.0070 0.0096</td></tr><tr><td>USFD20113</td><td>127</td><td colspan=\"2\">0.0079 0.0014 0.0024</td></tr></table>", |
| "type_str": "table", |
| "num": null, |
| "text": "" |
| } |
| } |
| } |
| } |
