Online community-based services such as web forums, message boards, and blogs provide an efficient and effective way for sharing information and gathering knowledge [1
]. In the field of mental health care, these services allow individuals to describe their life stresses and depressive problems to other Internet users or health professionals who can then make recommendations to help the subject developing the knowledge needed to seek appropriate care. Examples of these websites include Depression Forumsa
, and Yahoo!Answerse
. This paper refers to this type of online post as online psychiatric texts, and their major characteristic is that they are in the form of natural language texts, featuring many cause-effect relations between sentences. Some examples of causality sentences are presented below:
(E1) I couldn’t sleep for several days because my boss cut my salary.
(E2) I failed again. I felt very upset.
(E3) I broke up with my boyfriend. Life now is meaningless to me.
These examples indicate three depressive problems caused by negative life events experienced by the speaker. Awareness of such cause-effect relations between sentences can improve our understanding of users’ problems and make online psychiatric services more effective. For instance, systems capable of identifying causality from online forum posts could assist health professionals in capturing users’ background information more quickly, thus decreasing response time. Additionally, a dialog system could generate supportive responses if it could understand depressive problems and their associated reasons embedded in users’ input. Recent studies also show that causality is an important concept in biomedical informatics [4
], and identifying cause-effect relations as well as other semantic relations could improve the effectiveness of many applications such as question answering [5
], biomedical text mining [8
], future event prediction [11
], information retrieval [12
], and e-learning [13
]. Therefore, this paper proposes a text mining framework to detect cause-effect relations between sentences from online psychiatric texts.
Causality (or a cause-effect relation) is a relation between two events: cause and effect. In natural language texts, cause-effect relations can generally be categorized as explicit and implicit depending on whether or not a discourse connective (e.g., “because”, “therefore”) is found between the cause and effect text spans [14
]. For instance, the example sentence E1 contains an explicit cause-effect relation due to the presence of the discourse connective “because” which signals the relation. Conversely, both E2 and E3 lack a discourse connective and thus the cause-effect relation between the sentences is implicit. Traditional approaches to identifying explicit cause-effect relations have focused on mining useful discourse connectives that can trigger the cause-effect relation. Wu et al. [17
] manually collected a set of discourse connectives to identify cause-effect relations from psychiatric consultation records. Ramesh and Yu [18
] proposed the use of a supervised machine learning method called conditional random fields (CRFs) to automatically identify discourse connectives in biomedical texts. Inui et al. [19
] used a discourse connective “tame
” to acquire causal knowledge from Japanese newspaper articles. Although discourse connectives are useful features for identifying causality, the difficulty inherent in collecting a complete set of discourse connectives may result in this approach failing to identify the cause-effect relations triggered by unknown discourse connectives. In addition, it may also fail to identify implicit cause-effect relations that lack an explicit discourse connective between the sentences. Accordingly, other useful features and algorithms have been investigated to identify implicit causality within [20
] and between sentences [22
]. Efforts to identify causality within sentences have investigated features that consider sentence structure. Rink et al. [20
] proposed the use of textual graph patterns obtained from parse trees to determine whether two events from the same sentence have a causal relation. Mulkar-Mehta et al. [21
] introduced a theory of granularity to identify sentences containing causal relations. Features across the sentence boundary could be useful in identifying causality between sentences because such features can capture feature relationships between sentences. For instance, word pairs in which one word comes from the cause text span and the other comes from the effect text span have been demonstrated to be useful features for discovering implicit causality between sentences [22
] because they can capture individual word associations between cause and effect sentences. In the E2 sample sentence pair, the word pair (fail, upset) helps identify the implicit cause-effect relation that holds between the two sentences.
However, within the sentences, individual words usually cannot reflect the exact meaning of the cause and effect events which, taking E3 as an example, may produce semantically incomplete word pairs such as (broke up, life), (broke up, meaningless), (boyfriend, life), and (boyfriend, meaningless). In fact, many cause and effect events can be characterized by language patterns, i.e., meaningful combinations of words. For instance, in E3, the first sentence (cause) can be characterized by a language pattern < broke up, boyfriend>, and the second sentence (effect) can be characterized by < life, meaningless>. Combining these two intra-sentential language patterns constitutes a more semantically complete inter-sentential language pattern < <broke up, boyfriend>, <life, meaningless>>. Such inter-sentential language patterns can provide more precise information to improve the performance of causality detection because they can capture the associations of multiple words within and between sentences. Therefore, this study develops a text mining framework by extending the classical association rule mining algorithm [24
] such that it can mine inter-sentential language patterns by associating frequently co-occurred patterns across the sentence boundary. The discovered patterns are then incorporated into a probabilistic model to detect causality between sentences.
The rest of this paper is organized as follows. We first describe the framework for inter-sentential language pattern mining and causality detection. We then summarize the experimental results of and present conclusions.