acl acl2013 acl2013-62 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tyler Baldwin ; Yunyao Li ; Bogdan Alexe ; Ioana R. Stanoi
Abstract: While the resolution of term ambiguity is important for information extraction (IE) systems, the cost of resolving each instance of an entity can be prohibitively expensive on large datasets. To combat this, this work looks at ambiguity detection at the term, rather than the instance, level. By making a judgment about the general ambiguity of a term, a system is able to handle ambiguous and unambiguous cases differently, improving throughput and quality. To address the term ambiguity detection problem, we employ a model that combines data from language models, ontologies, and topic modeling. Results over a dataset of entities from four product domains show that the proposed approach achieves significantly above baseline F-measure of 0.96.
Reference: text
sentIndex sentText sentNum sentScore
1 com , rs Abstract While the resolution of term ambiguity is important for information extraction (IE) systems, the cost of resolving each instance of an entity can be prohibitively expensive on large datasets. [sent-4, score-1.037]
2 To combat this, this work looks at ambiguity detection at the term, rather than the instance, level. [sent-5, score-0.699]
3 By making a judgment about the general ambiguity of a term, a system is able to handle ambiguous and unambiguous cases differently, improving throughput and quality. [sent-6, score-1.0]
4 To address the term ambiguity detection problem, we employ a model that combines data from language models, ontologies, and topic modeling. [sent-7, score-1.027]
5 It can be particularly problematic for information extraction (IE), as IE systems often wish to extract information about only one sense of polysemous terms. [sent-12, score-0.185]
6 If nothing is done to account for this polysemy, frequent mentions of unrelated senses can drastically harm performance. [sent-13, score-0.103]
7 Several NLP tasks, such as word sense disambiguation, word sense induction, and named entity disambiguation, address this ambiguity problem to varying degrees. [sent-14, score-0.843]
8 While the goals and initial data assumptions vary between these tasks, all of them attempt to map an instance of a term seen in context to an individual sense. [sent-15, score-0.408]
9 While making a judgment for every instance may be appropri- ate for small or medium sized data sets, the cost of applying these ambiguity resolution procedures becomes prohibitively expensive on large data sets of tens to hundreds of million items. [sent-16, score-0.728]
10 To combat this, this work zooms out to examine the ambiguity problem at a more general level. [sent-17, score-0.589]
11 To do so, we define an IE-centered ambiguity detection problem, which ties the notion of ambiguity to a given topical domain. [sent-18, score-1.254]
12 For instance, given that the terms Call of Juarez and A New Beginning can both reference video games, we would like to discover that only the latter case is likely to appear frequently in non-video game contexts. [sent-19, score-0.262]
13 The goal is to make a binary decision as to whether, given a term and a domain, we can expect every instance of that term to reference an entity in that domain. [sent-20, score-0.68]
14 By doing so, we segregate ambiguous terms from their unambiguous counterparts. [sent-21, score-0.401]
15 Using this segregation allows ambiguous and unambiguous instances to be treated differently while saving the processing time that might normally be spent attempting to disambiguate individual instances of unambiguous terms. [sent-22, score-0.67]
16 Previous approaches to handling word ambiguity employ a variety of disparate methods, variously relying on structured ontologies, gleaming insight from general word usage patterns via language models, or clustering the contexts in which words appear. [sent-23, score-0.666]
17 This work employs an ambiguity detection pipeline that draws inspiration from all of these methods to achieve high performance. [sent-24, score-0.652]
18 2 Term Ambiguity Detection (TAD) A term can be ambiguous in many ways. [sent-25, score-0.429]
19 It may have non-referential senses in which it shares a name with a common word or phrase, such as in the films Brave and 2012. [sent-26, score-0.221]
20 A term may have referential senses across topical domains, such as The Girl with the Dragon Tattoo, which may reference either the book or the film adaptation. [sent-27, score-0.619]
21 c A2s0s1o3ci Aatsiosonc fioartio Cno fmorpu Ctoamtiopnuatalt Lioin gauli Lsitnicgsu,i psatgices 804–809, also be ambiguous within a topical domain. [sent-30, score-0.249]
22 For instance, the term Final Fantasy may refer to the video game franchise or one of several individual games within the franchise. [sent-31, score-0.586]
23 In this work we concern ourselves with the first two types of ambiguity, as within topical domain ambiguity tends to pose a less severe problem for IE systems. [sent-32, score-0.643]
24 IE systems are often asked to perform extraction over a dictionary of terms centered around a single topic. [sent-33, score-0.082]
25 With this use case in mind, we define the term ambiguity detection (TAD) problem as follows: Given a term and a corresponding topic domain, determine whether the term uniquely references a member of that topic domain. [sent-35, score-1.636]
26 That is, given a term such as Brave and a category such as film, the task is make a binary decision as to whether all instances of Brave reference a film by that name. [sent-36, score-0.423]
27 1 Framework Our TAD framework ing of three modules is primarily designed biguity. [sent-38, score-0.179]
28 This module is a hybrid approach consist(Figure 1). [sent-39, score-0.096]
29 The first module to detect non-referential amexamines n-gram data from a large text collection. [sent-40, score-0.134]
30 The rationale behind the n-gram module is based on the understanding that terms appearing in non-named entity contexts are likely to be nonreferential, and terms that can be non-referential are ambiguous. [sent-42, score-0.25]
31 Therefore, detecting terms that have non-referential usages can also be used to detect ambiguity. [sent-43, score-0.132]
32 Since we wish for the ambiguity detection determination to be fast, we develop our method to make this judgment solely on the n-gram probability, without the need to examine each individual usage context. [sent-44, score-0.947]
33 To do so, we assume that an all lowercased version of the term is a reasonable proxy for non-named entity usages in formal text. [sent-45, score-0.369]
34 If the probability is above a certain threshold, the term is labeled as ambiguous. [sent-47, score-0.271]
35 If the term is below the threshold, it is tentatively labeled as unambiguous and passed to the next module. [sent-48, score-0.464]
36 To avoid making judgments of ambiguity based on very infrequent uses, the ambiguous-unambiguous determination threshold is empirically determined by minimizing error over held out data. [sent-49, score-0.663]
37 The second module employs ontologies to detect across domain ambiguity. [sent-50, score-0.32]
38 Terms that have multiple senses in Wiktionary were labeled as ambiguous. [sent-53, score-0.103]
39 All terms that had a disambiguation page were marked as ambiguous. [sent-55, score-0.222]
40 The final module attempts to detect both nonreferential and across domain ambiguity by clustering the contexts in which words appear. [sent-56, score-0.822]
41 LDA represents a document as a distribution of topics, and each topic as a distribution of words. [sent-59, score-0.063]
42 As our domain of interest is Twitter, we performed clustering over a large collection of tweets. [sent-60, score-0.108]
43 For a given term, all tweets that contained the term were used as a document collection. [sent-61, score-0.378]
44 Following standard procedure, stopwords and infrequent words were removed before topic modeling was performed. [sent-62, score-0.134]
45 Since the clustering mechanism was designed to make predictions over the already filtered data of the other modules, it adopts a conservative approach to predicting ambiguity. [sent-63, score-0.119]
46 , film) or a synonym from the WordNet synset does not appear in the 10 most heavily weighted words for any cluster, the term is marked as ambiguous. [sent-66, score-0.311]
47 A term is labeled as ambiguous if any one of the three modules predicts that it is ambiguous, but only labeled as unambiguous if all three modules make this prediction. [sent-67, score-0.98]
48 This design allows each module to be relatively conservative in predicting ambiguity, keeping precision of ambiguity prediction high, under the assumption that other modules will compensate for the corresponding drop in recall. [sent-68, score-0.838]
49 1 Data Set Initial Term Sets We collected a data set of terms from four topical domains: books, films, video games, and cameras. [sent-70, score-0.243]
50 Terms for the first three domains are lists of books, films, and video games respectively from the years 2000-201 1 from dbpedia (Auer et al. [sent-71, score-0.258]
51 neTo,p5worstmovies verA B eaST ueptlirfcumel M indCaft ie l gm moryJudygnemosent Table 1: Example tweet annotations. [sent-78, score-0.231]
52 Figure 1: Overview of the ambiguity detection framework. [sent-79, score-0.652]
53 for cameras includes all the cameras from the six most popular brands on flickr2. [sent-80, score-0.122]
54 Gold Standard A set of 100 terms per domain were chosen at random from the initial term sets. [sent-81, score-0.393]
55 Rather than annotating each term directly, ambiguity was determined by examining actual usage. [sent-82, score-0.782]
56 Specifically, for each term, usage examples were extracted from large amounts of Twitter data. [sent-83, score-0.047]
57 Tweets for the video game andfilm categories were extracted from the TREC Twitter corpus. [sent-84, score-0.174]
58 3 The less common book and camera cases were extracted from a subset of all tweets from September 1st-9th, 2012. [sent-85, score-0.187]
59 For each term, two annotators were given the term, the corresponding topic domain, and 10 randomly selected tweets containing the term. [sent-86, score-0.17]
60 They were then asked to make a binary judgment as to whether the usage of the term in the tweet referred to an instance of the given category. [sent-87, score-0.517]
61 The degree of ambiguity is then determined by calculating the percentage of tweets that did not reference a member of the topic domain. [sent-88, score-0.764]
62 If all individual tweet judgments for a term were marked as referring to a 2http://www. [sent-90, score-0.519]
63 member of the topic domain, the term was marked as fully unambiguous within the data examined. [sent-102, score-0.612]
64 Most disagreements on individual tweet judgments had little effect on the final judgment of a term as ambiguous or unambiguous, and those that did were resolved internally. [sent-106, score-0.718]
65 2 Evaluation and Results Effectiveness To understand the contribution of the n-gram (NG), ontology (ON), and clustering (CL) based modules, we ran each separately, as well as every possible combination. [sent-108, score-0.131]
66 Of the three individual modules, the ngram and clustering methods achieve F-measure of around 0. [sent-111, score-0.127]
67 9, while the ontology-based module performs only modestly above baseline. [sent-112, score-0.096]
68 Unsurprisingly, the ontology method is affected heavily by its coverage, so its poor performance is primarily attributable to low recall. [sent-113, score-0.064]
69 Additionally, ontologies may be apt to list cases of strict ambiguity, rather than practical ambiguity. [sent-115, score-0.145]
70 That is, an ontology may list a term as ambiguous if there are 4The annotated data is available at http / / re s earche r . [sent-116, score-0.493]
71 Combining any two methods produced substantial performance increases over any of the individual runs. [sent-123, score-0.06]
72 The final system that employed all modules produced an F-measure of 0. [sent-124, score-0.179]
73 Usefulness To establish that term ambiguity detection is actually helpful for IE, we conducted a preliminary study by integrating our pipeline into a commercially available rule-based IE system (Chiticariu et al. [sent-128, score-0.923]
74 The system takes a list of product names as input and outputs tweets associated with each product. [sent-131, score-0.107]
75 It utilizes rules that employ more conservative extraction for ambiguous entities. [sent-132, score-0.283]
76 Experiments were conducted over several million tweets using the terms from the video game and camera domains. [sent-133, score-0.376]
77 When no ambiguity detection was performed, all terms were treated as unambiguous. [sent-134, score-0.702]
78 16 when no ambiguity detection was used, due to the extraction of irrelevant instances of ambiguous objects. [sent-136, score-0.875]
79 However, the inclusion of disambiguation did reduce the overall recall; the system that employed disambiguation returned only about 57% of the true positives returned by the system that did not employ disambiguation. [sent-139, score-0.367]
80 Although this reduction in recall is significant, the overall impact of disambiguation is clearly positive, due to the stark difference in precision. [sent-140, score-0.132]
81 Machine translation systems can suffer, as ambiguity in the source language may lead to incorrect translations, and unambiguous sentences in one language may become am- biguous in another (Carpuat and Wu, 2007; Chan et al. [sent-143, score-0.704]
82 The ambiguity detection problem is similar to the well studied problems of named entity disambiguation (NED) and word sense disambiguation (WSD). [sent-146, score-1.127]
83 However, these tasks assume that the number of senses a word has is given, essentially assuming that the ambiguity detection problem has already been solved. [sent-147, score-0.755]
84 This makes these tasks inapplicable in many IE instances where the amount of ambiguity is not known ahead of time. [sent-148, score-0.544]
85 Both named entity and word sense disambiguation are extensively studied, and surveys on each are available (Nadeau and Sekine, 2007; Navigli, 2009). [sent-149, score-0.343]
86 Another task that shares similarities with TAD is word sense induction (WSI). [sent-150, score-0.217]
87 Like NED and WSD, WSI frames the ambiguity problem as one of determining the sense of each individual instance, rather than the term as a whole. [sent-151, score-0.963]
88 Unlike those approaches, the word sense induction task attempts to both figure out the number of senses a word has, and what they are. [sent-152, score-0.287]
89 Pantel and Lin (2002) employ a clustering by committee method that iteratively adds words to clusters based on their similarities. [sent-155, score-0.108]
90 5 Conclusion This paper introduced the term ambiguity detection task, which detects whether a term is ambiguous relative to a topical domain. [sent-160, score-1.443]
91 Unlike other ambiguity resolution tasks, the ambiguity detection problem makes general ambiguity judgments about terms, rather than resolving individual instances. [sent-161, score-1.889]
92 By doing so, it eliminates the need for ambiguity resolution on unambiguous objects, allowing for increased throughput of IE systems on large data sets. [sent-162, score-0.807]
93 Our solution for the term ambiguity detection 807 task is based on a combined model with three distinct modules based on n-grams, ontologies, and clustering. [sent-163, score-1.102]
94 Our initial study suggests that the combination of different modules designed for different types of ambiguity used in our solution is effective in determining whether a term is ambiguous for a given domain. [sent-164, score-1.15]
95 Although the task as presented here was motivated with information extraction in mind, it is possible that term ambiguity detection could be useful for other tasks. [sent-166, score-0.955]
96 For instance, TAD could be used to aid word sense induction more generally, or could be applied as part of other tasks such as coreference resolution. [sent-167, score-0.184]
97 Chinese verb sense discrimination using an em clustering model with rich linguistic features. [sent-202, score-0.188]
98 Word sense induction & disambiguation using hierarchical random graphs. [sent-218, score-0.316]
99 Inducing word senses to improve web search result clustering. [sent-232, score-0.103]
100 Query ambiguity revisited: Clickthrough measures for distinguishing informational and ambiguous queries. [sent-253, score-0.669]
wordName wordTfidf (topN-words)
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